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8226 lines
979 KiB
8226 lines
979 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "263a619c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"New https://pypi.org/project/ultralytics/8.3.235 available 😃 Update with 'pip install -U ultralytics'\n",
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"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=-1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/home/cuuva/experiment/fashion_yolo/aihub_fashion.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=200, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8s.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=yolov8s_fashion, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=40, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=aihub_exp, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
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"Overriding model.yaml nc=80 with nc=9\n",
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"\n",
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" from n params module arguments \n",
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" 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n",
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" 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
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" 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] \n",
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" 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
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" 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
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" 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
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" 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] \n",
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" 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n",
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" 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] \n",
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" 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n",
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" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
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" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] \n",
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" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
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" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
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" 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
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" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
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" 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n",
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" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
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" 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] \n",
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" 22 [15, 18, 21] 1 2119531 ultralytics.nn.modules.head.Detect [9, [128, 256, 512]] \n",
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"Model summary: 129 layers, 11,139,083 parameters, 11,139,067 gradients, 28.7 GFLOPs\n",
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"\n",
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"Transferred 349/355 items from pretrained weights\n",
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"Freezing layer 'model.22.dfl.conv.weight'\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
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"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 21065.6±11622.9 MB/s, size: 2073.5 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/clothes_dataset/Training/labels/task1_yolo_dispose_BLOUSE.cache... 80000 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 80000/80000 166.9Mit/s 0.0s\n",
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"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.36G total, 0.17G reserved, 0.11G allocated, 31.07G free\n",
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" Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/cuuva/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 11139083 28.66 1.715 27.4 131.9 (1, 3, 640, 640) list\n",
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" 11139083 57.33 2.252 5.365 23.35 (2, 3, 640, 640) list\n",
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" 11139083 114.7 2.412 6.467 27.61 (4, 3, 640, 640) list\n",
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" 11139083 229.3 3.446 8.65 36.54 (8, 3, 640, 640) list\n",
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" 11139083 458.6 6.088 13.39 58.79 (16, 3, 640, 640) list\n",
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" 11139083 917.3 10.503 27.94 98.08 (32, 3, 640, 640) list\n",
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" 11139083 1835 19.724 57.47 183.7 (64, 3, 640, 640) list\n",
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"\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 61 for CUDA:0 19.12G/31.36G (61%) ✅\n",
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"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 12772.5±4201.8 MB/s, size: 3291.8 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/aihub_car/clothes_dataset/Training/labels/task1_yolo_dispose_BLOUSE.cache... 80000 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 80000/80000 163.0Mit/s 0.0s\n",
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"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
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"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1093.3±76.4 MB/s, size: 2293.4 KB)\n",
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"\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/aihub_car/clothes_dataset/Validation/labels/task1_yolo_dispose_BLOUSE.cache... 10000 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 10000/10000 16.2Mit/s 0.0s\n",
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"Plotting labels to /home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion/labels.jpg... \n",
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"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0004765625), 63 bias(decay=0.0)\n",
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"Image sizes 640 train, 640 val\n",
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"Using 8 dataloader workers\n",
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"Logging results to \u001b[1m/home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion\u001b[0m\n",
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"Starting training for 200 epochs...\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 1/200 15.4G 0.3159 0.7355 0.97 75 640: 100% ━━━━━━━━━━━━ 1312/1312 2.5it/s 8:41<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.9it/s 16.9s0.2s\n",
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" all 10000 10000 0.787 0.783 0.833 0.8\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 2/200 15.4G 0.3112 0.5777 0.9604 79 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:10<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.1it/s 19.8s0.2ss\n",
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" all 10000 10000 0.735 0.789 0.856 0.848\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 3/200 15.4G 0.2982 0.5394 0.9531 67 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:12<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 3.7it/s 22.1s0.3s\n",
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" all 10000 10000 0.842 0.85 0.912 0.905\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 4/200 15.4G 0.2806 0.4979 0.946 81 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:15<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.1it/s 20.0s0.2s\n",
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" all 10000 10000 0.88 0.877 0.934 0.932\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 5/200 15.4G 0.2685 0.4621 0.9404 72 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.4it/s 18.6s0.2s\n",
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" all 10000 10000 0.882 0.898 0.945 0.944\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 6/200 15.4G 0.2589 0.4416 0.9363 70 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:12<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.5it/s 18.3s0.2s\n",
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" all 10000 10000 0.905 0.896 0.95 0.949\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 7/200 15.4G 0.2526 0.4271 0.9336 67 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:10<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.7it/s 17.4s0.2s\n",
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" all 10000 10000 0.898 0.912 0.956 0.956\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 8/200 15.4G 0.249 0.4116 0.9327 80 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:11<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.7it/s 17.5s0.2s\n",
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" all 10000 10000 0.912 0.909 0.959 0.959\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 9/200 15.4G 0.2472 0.4026 0.9319 79 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:16<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.5it/s 18.3s0.2s\n",
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" all 10000 10000 0.904 0.92 0.96 0.96\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 10/200 15.4G 0.2414 0.3935 0.9288 75 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.7it/s 17.5s0.3s\n",
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" all 10000 10000 0.917 0.913 0.961 0.961\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 11/200 15.4G 0.2369 0.3808 0.927 73 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:14<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.4it/s 18.6s0.2s\n",
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" all 10000 10000 0.914 0.915 0.963 0.963\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 12/200 15.4G 0.2347 0.3767 0.9259 78 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.4it/s 18.5s0.2s\n",
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" all 10000 10000 0.914 0.914 0.964 0.963\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 13/200 15.4G 0.2325 0.3699 0.9249 86 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:15<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.5it/s 18.4s0.2s\n",
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" all 10000 10000 0.902 0.927 0.964 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 14/200 15.4G 0.2305 0.3635 0.9247 87 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 3.9it/s 21.1s0.3s\n",
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" all 10000 10000 0.914 0.918 0.964 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 15/200 15.4G 0.228 0.3587 0.9233 74 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:14<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.4it/s 18.7s0.2s\n",
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" all 10000 10000 0.917 0.918 0.964 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 16/200 15.4G 0.2273 0.3549 0.9228 69 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:12<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.3it/s 18.9s0.3s\n",
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" all 10000 10000 0.915 0.92 0.964 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 17/200 15.4G 0.2274 0.3507 0.9232 74 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:14<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.3it/s 19.2s0.3s\n",
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" all 10000 10000 0.918 0.917 0.965 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 18/200 15.4G 0.2268 0.3474 0.9235 67 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:15<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.2it/s 19.5s0.2s\n",
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" all 10000 10000 0.915 0.918 0.965 0.964\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 19/200 15.4G 0.2242 0.3439 0.9217 70 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.2it/s 19.7s0.2s\n",
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" all 10000 10000 0.913 0.92 0.965 0.965\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 20/200 15.4G 0.2234 0.3396 0.9218 87 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:16<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 3.9it/s 21.1s0.2s\n",
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" all 10000 10000 0.905 0.927 0.965 0.965\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 21/200 15.4G 0.2216 0.3376 0.9203 82 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:16<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.1it/s 19.8s0.2s\n",
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" all 10000 10000 0.909 0.922 0.965 0.965\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 22/200 15.4G 0.2208 0.3317 0.9208 76 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:16<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.1it/s 20.1s0.3s\n",
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" all 10000 10000 0.909 0.924 0.965 0.965\n",
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"\n",
|
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 23/200 15.4G 0.221 0.3307 0.9208 76 640: 100% ━━━━━━━━━━━━ 1312/1312 2.6it/s 8:16<0.2s\n",
|
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.0it/s 20.3s0.3s\n",
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" all 10000 10000 0.907 0.925 0.965 0.965\n",
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"\n",
|
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 24/200 15.4G inf 0.3275 0.9202 69 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:14<0.2s\n",
|
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.0it/s 20.3s0.3s\n",
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" all 10000 10000 0.909 0.923 0.965 0.965\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 25/200 15.4G 0.2193 0.3237 0.9201 77 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:13<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 3.9it/s 21.2s0.3s\n",
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" all 10000 10000 0.917 0.918 0.965 0.965\n",
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"\n",
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 26/200 15.4G 0.2178 0.3203 0.9188 74 640: 100% ━━━━━━━━━━━━ 1312/1312 2.7it/s 8:14<0.2s\n",
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"\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 82/82 4.3it/s 19.3s0.3s\n",
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" all 10000 10000 0.916 0.919 0.965 0.965\n",
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"\n",
|
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" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
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"\u001b[K 27/200 15.4G 0.2203 0.3182 0.9208 151 640: 22% ━━╸───────── 287/1312 3.6it/s 1:59<4:438\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 4\u001b[0m model \u001b[38;5;241m=\u001b[39m YOLO(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myolov8s.pt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# 2. 학습 실행\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# 위에서 생성된 yaml 파일 경로를 넣어줍니다.\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m train_results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/home/cuuva/experiment/fashion_yolo/aihub_fashion.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Fashionpedia는 데이터가 많고 복잡해서 epoch을 조절해야 할 수도 있어\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m640\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# GPU 메모리에 맞춰 조절 (-1은 AutoBatch)\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAdamW\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mproject\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maihub_exp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myolov8s_fashion\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/model.py:777\u001b[0m, in \u001b[0;36mModel.train\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mget_model(weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mckpt \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, cfg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39myaml)\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m--> 777\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 778\u001b[0m \u001b[38;5;66;03m# Update model and cfg after training\u001b[39;00m\n\u001b[1;32m 779\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m}:\n",
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"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/trainer.py:238\u001b[0m, in \u001b[0;36mBaseTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 235\u001b[0m ddp_cleanup(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mstr\u001b[39m(file))\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/trainer.py:431\u001b[0m, in \u001b[0;36mBaseTrainer._do_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler\u001b[38;5;241m.\u001b[39mscale(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss)\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ni \u001b[38;5;241m-\u001b[39m last_opt_step \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccumulate:\n\u001b[0;32m--> 431\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 432\u001b[0m last_opt_step \u001b[38;5;241m=\u001b[39m ni\n\u001b[1;32m 434\u001b[0m \u001b[38;5;66;03m# Timed stopping\u001b[39;00m\n",
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"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/ultralytics/engine/trainer.py:678\u001b[0m, in \u001b[0;36mBaseTrainer.optimizer_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler\u001b[38;5;241m.\u001b[39munscale_(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer) \u001b[38;5;66;03m# unscale gradients\u001b[39;00m\n\u001b[1;32m 677\u001b[0m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mclip_grad_norm_(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mparameters(), max_norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m)\n\u001b[0;32m--> 678\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscaler\u001b[38;5;241m.\u001b[39mupdate()\n\u001b[1;32m 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
|
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"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/amp/grad_scaler.py:462\u001b[0m, in \u001b[0;36mGradScaler.step\u001b[0;34m(self, optimizer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munscale_(optimizer)\n\u001b[1;32m 458\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound_inf_per_device\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, (\n\u001b[1;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo inf checks were recorded for this optimizer.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 460\u001b[0m )\n\u001b[0;32m--> 462\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_opt_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstage\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m OptState\u001b[38;5;241m.\u001b[39mSTEPPED\n\u001b[1;32m 466\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
|
|
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/amp/grad_scaler.py:356\u001b[0m, in \u001b[0;36mGradScaler._maybe_opt_step\u001b[0;34m(self, optimizer, optimizer_state, *args, **kwargs)\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_maybe_opt_step\u001b[39m(\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 350\u001b[0m optimizer: torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mOptimizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 354\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 355\u001b[0m retval: Optional[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43msum\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moptimizer_state\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfound_inf_per_device\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 357\u001b[0m retval \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mstep(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
|
|
"File \u001b[0;32m~/anaconda3/envs/1stagedetect/lib/python3.10/site-packages/torch/amp/grad_scaler.py:356\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_maybe_opt_step\u001b[39m(\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 350\u001b[0m optimizer: torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mOptimizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 354\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 355\u001b[0m retval: Optional[\u001b[38;5;28mfloat\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28msum\u001b[39m(\u001b[43mv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m optimizer_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound_inf_per_device\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 357\u001b[0m retval \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mstep(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retval\n",
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from ultralytics import YOLO\n",
|
|
"\n",
|
|
"# 1. 모델 로드 (YOLOv8m 사용)\n",
|
|
"model = YOLO('yolov8s.pt')\n",
|
|
"\n",
|
|
"# 2. 학습 실행\n",
|
|
"# 위에서 생성된 yaml 파일 경로를 넣어줍니다.\n",
|
|
"train_results = model.train(\n",
|
|
" data=\"/home/cuuva/experiment/fashion_yolo/aihub_fashion.yaml\", \n",
|
|
" epochs=200, # Fashionpedia는 데이터가 많고 복잡해서 epoch을 조절해야 할 수도 있어\n",
|
|
" imgsz=640,\n",
|
|
" batch=-1, # GPU 메모리에 맞춰 조절 (-1은 AutoBatch)\n",
|
|
" device=\"cuda\",\n",
|
|
" optimizer='AdamW',\n",
|
|
" lr0=0.001,\n",
|
|
" patience=40,\n",
|
|
" project='aihub_exp',\n",
|
|
" name='yolov8s_fashion',\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "b45cbf28",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"🔄 Inference 시작...\n",
|
|
"🎉 완료! 저장 위치: /home/cuuva/다운로드/infer_clothes.mp4\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from ultralytics import YOLO\n",
|
|
"import cv2\n",
|
|
"\n",
|
|
"# ---- 경로 설정 ----\n",
|
|
"model_path = \"/home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion/weights/best.pt\"\n",
|
|
"input_video_path = \"/home/cuuva/다운로드/face_recog.MOV\"\n",
|
|
"output_video_path = \"/home/cuuva/다운로드/infer_clothes.mp4\"\n",
|
|
"\n",
|
|
"# ---- 모델 로드 ----\n",
|
|
"model = YOLO(model_path)\n",
|
|
"\n",
|
|
"# ---- 입력 영상 로드 ----\n",
|
|
"cap = cv2.VideoCapture(input_video_path)\n",
|
|
"\n",
|
|
"# 영상 정보 읽기\n",
|
|
"fps = cap.get(cv2.CAP_PROP_FPS)\n",
|
|
"width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
|
|
"height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
|
|
"\n",
|
|
"# ---- 출력 영상 저장 설정 ----\n",
|
|
"fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n",
|
|
"out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))\n",
|
|
"\n",
|
|
"print(f\"🔄 Inference 시작...\")\n",
|
|
"\n",
|
|
"while cap.isOpened():\n",
|
|
" ret, frame = cap.read()\n",
|
|
" if not ret:\n",
|
|
" break\n",
|
|
"\n",
|
|
" # ---- YOLO 추론 ----\n",
|
|
" results = model(frame, verbose=False)\n",
|
|
"\n",
|
|
" # YOLO 결과 이미지 렌더링\n",
|
|
" annotated_frame = results[0].plot()\n",
|
|
"\n",
|
|
" # ---- 영상 저장 ----\n",
|
|
" out.write(annotated_frame)\n",
|
|
"\n",
|
|
"# ---- 자원 정리 ----\n",
|
|
"cap.release()\n",
|
|
"out.release()\n",
|
|
"\n",
|
|
"print(f\"🎉 완료! 저장 위치: {output_video_path}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f338c8c1",
|
|
"metadata": {},
|
|
"source": [
|
|
"# AIHUB로 Inference하면 성능 개차반. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "e8a7e835",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"🔍 이미지 검사 중...\n",
|
|
"📌 조건 충족 이미지 개수: 45533 개\n",
|
|
"📦 최종 복사할 이미지 수: 300\n",
|
|
"\n",
|
|
"🎉 완료! 양자화용 데이터가 준비됨.\n",
|
|
"📁 저장 위치 → /home/cuuva/다운로드/quan_fashion_face/external1\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import os\n",
|
|
"import random\n",
|
|
"import shutil\n",
|
|
"from PIL import Image\n",
|
|
"\n",
|
|
"# ---- 경로 설정 ----\n",
|
|
"dataset_root = \"/home/cuuva/experiment/datasets/fashionpedia_yolo\"\n",
|
|
"image_dir = os.path.join(dataset_root, \"images/train/\")\n",
|
|
"label_dir = os.path.join(dataset_root, \"labels/train/\")\n",
|
|
"\n",
|
|
"output_dir = \"/home/cuuva/다운로드/quan_fashion_face/external1\"\n",
|
|
"os.makedirs(output_dir, exist_ok=True)\n",
|
|
"\n",
|
|
"# ---- 필터 기준 ----\n",
|
|
"MIN_RESOLUTION = 300 # 최소 해상도 조건\n",
|
|
"REQUIRED_COUNT = 300 # 최종 복사 개수\n",
|
|
"\n",
|
|
"valid_images = []\n",
|
|
"\n",
|
|
"print(\"🔍 이미지 검사 중...\")\n",
|
|
"\n",
|
|
"for img_name in os.listdir(image_dir):\n",
|
|
" if not img_name.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
|
|
" continue\n",
|
|
"\n",
|
|
" img_path = os.path.join(image_dir, img_name)\n",
|
|
" label_path = os.path.join(label_dir, os.path.splitext(img_name)[0] + \".txt\")\n",
|
|
"\n",
|
|
" # 1) 라벨 파일 존재하는지\n",
|
|
" if not os.path.exists(label_path):\n",
|
|
" continue\n",
|
|
"\n",
|
|
" # 2) 라벨에 객체가 1개 이상인지\n",
|
|
" with open(label_path, \"r\") as f:\n",
|
|
" lines = f.readlines()\n",
|
|
" if len(lines) == 0:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" # 3) 해상도 체크\n",
|
|
" try:\n",
|
|
" img = Image.open(img_path)\n",
|
|
" w, h = img.size\n",
|
|
" if min(w, h) < MIN_RESOLUTION:\n",
|
|
" continue\n",
|
|
" except:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" valid_images.append(img_path)\n",
|
|
"\n",
|
|
"print(f\"📌 조건 충족 이미지 개수: {len(valid_images)} 개\")\n",
|
|
"\n",
|
|
"# ---- 300장 샘플링 ----\n",
|
|
"if len(valid_images) < REQUIRED_COUNT:\n",
|
|
" print(f\"⚠️ 조건 맞는 이미지가 {REQUIRED_COUNT}개보다 적습니다. ({len(valid_images)}개)\")\n",
|
|
" sample_images = valid_images # 가능한 만큼만 복사\n",
|
|
"else:\n",
|
|
" sample_images = random.sample(valid_images, REQUIRED_COUNT)\n",
|
|
"\n",
|
|
"print(f\"📦 최종 복사할 이미지 수: {len(sample_images)}\")\n",
|
|
"\n",
|
|
"# ---- 복사 실행 ----\n",
|
|
"for img_path in sample_images:\n",
|
|
" shutil.copy(img_path, output_dir)\n",
|
|
"\n",
|
|
"print(\"\\n🎉 완료! 양자화용 데이터가 준비됨.\")\n",
|
|
"print(f\"📁 저장 위치 → {output_dir}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "08f995f3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"🔍 이미지 검사 중...\n",
|
|
"\n",
|
|
"📌 가로(W>H) 이미지: 5263개\n",
|
|
"📌 세로(H>W) 이미지: 40341개\n",
|
|
"\n",
|
|
"🎯 목표 비율 → Wide: 300장 / Tall: 0장\n",
|
|
"\n",
|
|
"📦 최종 선택된 이미지 수: 300\n",
|
|
"\n",
|
|
"🎉 완료! 비율 조정된 양자화용 이미지셋 생성됨!\n",
|
|
"📁 저장 위치 → /home/cuuva/다운로드/quan_fashion_face/external1\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import os\n",
|
|
"import random\n",
|
|
"import shutil\n",
|
|
"from PIL import Image\n",
|
|
"\n",
|
|
"# ---- 경로 설정 ----\n",
|
|
"dataset_root = \"/home/cuuva/experiment/datasets/fashionpedia_yolo\"\n",
|
|
"image_dir = os.path.join(dataset_root, \"images/train/\")\n",
|
|
"label_dir = os.path.join(dataset_root, \"labels/train/\")\n",
|
|
"\n",
|
|
"output_dir = \"/home/cuuva/다운로드/quan_fashion_face/external1\"\n",
|
|
"os.makedirs(output_dir, exist_ok=True)\n",
|
|
"\n",
|
|
"# ---- 필터 기준 ----\n",
|
|
"MIN_RESOLUTION = 300\n",
|
|
"REQUIRED_COUNT = 300\n",
|
|
"RATIO_TARGET_WIDE = 1 # 80%\n",
|
|
"RATIO_TARGET_TALL = 0 # 20%\n",
|
|
"\n",
|
|
"valid_wide_images = []\n",
|
|
"valid_tall_images = []\n",
|
|
"\n",
|
|
"print(\"🔍 이미지 검사 중...\")\n",
|
|
"\n",
|
|
"# ---- 이미지와 라벨 검증 ----\n",
|
|
"for img_name in os.listdir(image_dir):\n",
|
|
" if not img_name.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
|
|
" continue\n",
|
|
"\n",
|
|
" img_path = os.path.join(image_dir, img_name)\n",
|
|
" label_path = os.path.join(label_dir, os.path.splitext(img_name)[0] + \".txt\")\n",
|
|
"\n",
|
|
" if not os.path.exists(label_path):\n",
|
|
" continue\n",
|
|
"\n",
|
|
" try:\n",
|
|
" with open(label_path, \"r\") as f:\n",
|
|
" if len(f.readlines()) == 0:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" img = Image.open(img_path)\n",
|
|
" w, h = img.size\n",
|
|
" if min(w, h) < MIN_RESOLUTION:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" # ---- 가로/세로 비율 분류 ----\n",
|
|
" if w > h:\n",
|
|
" valid_wide_images.append(img_path)\n",
|
|
" else:\n",
|
|
" valid_tall_images.append(img_path)\n",
|
|
"\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"⚠️ 오류 발생: {e}\")\n",
|
|
" continue\n",
|
|
"\n",
|
|
"print(f\"\\n📌 가로(W>H) 이미지: {len(valid_wide_images)}개\")\n",
|
|
"print(f\"📌 세로(H>W) 이미지: {len(valid_tall_images)}개\")\n",
|
|
"\n",
|
|
"# ---- 목표 개수 계산 ----\n",
|
|
"target_wide = int(REQUIRED_COUNT * RATIO_TARGET_WIDE)\n",
|
|
"target_tall = REQUIRED_COUNT - target_wide\n",
|
|
"\n",
|
|
"print(f\"\\n🎯 목표 비율 → Wide: {target_wide}장 / Tall: {target_tall}장\")\n",
|
|
"\n",
|
|
"# 부족하면 가능한 만큼만\n",
|
|
"selected_wide = random.sample(valid_wide_images, min(len(valid_wide_images), target_wide))\n",
|
|
"selected_tall = random.sample(valid_tall_images, min(len(valid_tall_images), target_tall))\n",
|
|
"\n",
|
|
"# 만약 어느 한쪽이 부족하면 다른쪽에서 채우기\n",
|
|
"remaining = REQUIRED_COUNT - (len(selected_wide) + len(selected_tall))\n",
|
|
"if remaining > 0:\n",
|
|
" pool = valid_wide_images + valid_tall_images\n",
|
|
" remaining_pick = random.sample(pool, min(len(pool), remaining))\n",
|
|
" selected_images = selected_wide + selected_tall + remaining_pick\n",
|
|
"else:\n",
|
|
" selected_images = selected_wide + selected_tall\n",
|
|
"\n",
|
|
"print(f\"\\n📦 최종 선택된 이미지 수: {len(selected_images)}\")\n",
|
|
"\n",
|
|
"# ---- 복사 실행 ----\n",
|
|
"for img_path in selected_images:\n",
|
|
" shutil.copy(img_path, output_dir)\n",
|
|
"\n",
|
|
"print(\"\\n🎉 완료! 비율 조정된 양자화용 이미지셋 생성됨!\")\n",
|
|
"print(f\"📁 저장 위치 → {output_dir}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "ac390117",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"🔧 이미지 리사이즈 중...\n",
|
|
"\n",
|
|
"🎉 완료! 총 300개의 이미지를 640x384 로 변환했습니다.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from PIL import Image\n",
|
|
"import os\n",
|
|
"\n",
|
|
"# 리사이즈 대상 폴더\n",
|
|
"input_dir = \"/home/cuuva/다운로드/quan_fashion_face/external1\"\n",
|
|
"\n",
|
|
"# 원하는 출력 크기\n",
|
|
"TARGET_SIZE = (640, 384) # (width, height)\n",
|
|
"\n",
|
|
"print(\"🔧 이미지 리사이즈 중...\")\n",
|
|
"\n",
|
|
"count = 0\n",
|
|
"\n",
|
|
"for filename in os.listdir(input_dir):\n",
|
|
" if filename.lower().endswith((\".jpg\", \".jpeg\", \".png\")):\n",
|
|
" img_path = os.path.join(input_dir, filename)\n",
|
|
" try:\n",
|
|
" img = Image.open(img_path)\n",
|
|
"\n",
|
|
" # 리사이즈 (BILINEAR 방식이 가장 무난함)\n",
|
|
" resized_img = img.resize(TARGET_SIZE, Image.BILINEAR)\n",
|
|
"\n",
|
|
" # 덮어쓰기 저장\n",
|
|
" resized_img.save(img_path, quality=95)\n",
|
|
"\n",
|
|
" count += 1\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"⚠️ {filename} 처리 중 오류: {e}\")\n",
|
|
"\n",
|
|
"print(f\"\\n🎉 완료! 총 {count}개의 이미지를 {TARGET_SIZE[0]}x{TARGET_SIZE[1]} 로 변환했습니다.\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "2f4f5747",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"▶ Processing... please wait\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 2 pantss, 2 coats, 28.0ms\n",
|
|
"Speed: 1.3ms preprocess, 28.0ms inference, 43.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 3.5ms preprocess, 16.1ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 coat, 14.8ms\n",
|
|
"Speed: 2.4ms preprocess, 14.8ms inference, 6.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 2 coats, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 115.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 coat, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 16.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 46.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 37.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 39.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 90.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 42.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 89.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 coat, 12.6ms\n",
|
|
"Speed: 0.9ms preprocess, 12.6ms inference, 53.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 39.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 14.1ms\n",
|
|
"Speed: 0.9ms preprocess, 14.1ms inference, 51.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 40.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 12.3ms\n",
|
|
"Speed: 1.0ms preprocess, 12.3ms inference, 49.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 3 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 46.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 3 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 104.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 31.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 8.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 14.1ms\n",
|
|
"Speed: 1.3ms preprocess, 14.1ms inference, 50.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 14.3ms\n",
|
|
"Speed: 3.0ms preprocess, 14.3ms inference, 3.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 15.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.0ms\n",
|
|
"Speed: 3.4ms preprocess, 16.0ms inference, 37.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 3 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 3 pantss, 1 coat, 14.3ms\n",
|
|
"Speed: 1.0ms preprocess, 14.3ms inference, 51.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 89.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 33.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 13.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 13.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 pantss, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 3 pantss, 8.1ms\n",
|
|
"Speed: 1.1ms preprocess, 8.1ms inference, 15.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 73.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 19.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 71.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 3.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 3.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 22.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 38.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 1.2ms preprocess, 15.9ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 46.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 24.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 9.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 3.3ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 19.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 16.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.3ms\n",
|
|
"Speed: 2.5ms preprocess, 16.3ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 40.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 22.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 3.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 25.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.8ms preprocess, 16.0ms inference, 15.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 31.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 16.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 9.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 14.1ms\n",
|
|
"Speed: 1.0ms preprocess, 14.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 43.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 10.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 coats, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 14.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 coats, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 59.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 coats, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 12.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 10.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 7.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 24.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 16.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 21.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 8.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 24.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 38.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 37.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 10.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 3.1ms preprocess, 15.9ms inference, 21.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 1.9ms preprocess, 15.9ms inference, 29.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 4.1ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 21.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 42.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 10.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.7ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 23.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 16.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 1 coat, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 35.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 21.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 12.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 15.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 18.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 23.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 23.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 44.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 62.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 14.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 21.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 coat, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 38.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 14.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 16.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 24.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 8.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 5.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 46.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 23.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 7.8ms\n",
|
|
"Speed: 1.7ms preprocess, 7.8ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 45.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 15.9ms\n",
|
|
"Speed: 3.7ms preprocess, 15.9ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 15.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 35.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 32.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 42.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 12.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 39.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 17.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 35.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 42.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 4.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 33.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 36.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 8.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 38.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 20.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 44.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 37.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 22.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 3.1ms preprocess, 15.9ms inference, 13.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.1ms\n",
|
|
"Speed: 2.2ms preprocess, 15.1ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 37.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 19.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 48.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 42.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 33.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 33.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 6.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 32.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 14.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 33.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 28.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 31.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 1.7ms preprocess, 15.9ms inference, 17.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 42.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 33.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 30.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 4.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.3ms preprocess, 15.9ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 35.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 42.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 9.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 32.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 35.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 11.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 27.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 1.6ms preprocess, 15.9ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 42.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 14.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 35.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 35.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.3ms preprocess, 15.9ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 15.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 26.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 3.2ms preprocess, 15.9ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 30.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 15.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 40.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 6.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 29.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 13.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 24.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 4.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 30.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 37.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 15.3ms\n",
|
|
"Speed: 2.8ms preprocess, 15.3ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 28.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 15.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 27.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 4.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 13.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 32.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 26.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 15.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 19.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 38.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 12.7ms\n",
|
|
"Speed: 2.0ms preprocess, 12.7ms inference, 3.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 21.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 16.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 31.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 32.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 15.8ms\n",
|
|
"Speed: 1.6ms preprocess, 15.8ms inference, 16.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 35.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 19.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 41.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 5.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 24.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 13.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 20.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 15.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 6.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 2.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 15.5ms\n",
|
|
"Speed: 1.3ms preprocess, 15.5ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 11.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.2ms\n",
|
|
"Speed: 3.3ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 2.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 15.9ms\n",
|
|
"Speed: 2.3ms preprocess, 15.9ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 11.8ms\n",
|
|
"Speed: 1.0ms preprocess, 11.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.3ms\n",
|
|
"Speed: 3.0ms preprocess, 16.3ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 9.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 7.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 10.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 7.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 14.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 1.6ms preprocess, 15.9ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 7.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 4.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 9.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 5.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.5ms\n",
|
|
"Speed: 1.6ms preprocess, 15.5ms inference, 4.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 7.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 13.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 32.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 18.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 1.5ms preprocess, 15.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 14.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 3.5ms preprocess, 15.9ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 39.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 5.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 3.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 5.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 15.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 2.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 3.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 25.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 24.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 10.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 27.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 6.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 2.7ms preprocess, 15.9ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 13.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.1ms\n",
|
|
"Speed: 2.0ms preprocess, 14.1ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 14.1ms\n",
|
|
"Speed: 2.9ms preprocess, 14.1ms inference, 10.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.0ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 11.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 15.9ms\n",
|
|
"Speed: 1.7ms preprocess, 15.9ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 7.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 15.9ms\n",
|
|
"Speed: 3.0ms preprocess, 15.9ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 15.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 15.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 5.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 12.5ms\n",
|
|
"Speed: 1.0ms preprocess, 12.5ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 2.9ms preprocess, 15.9ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 17.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.5ms\n",
|
|
"Speed: 2.3ms preprocess, 15.5ms inference, 2.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 7.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 pants, 14.2ms\n",
|
|
"Speed: 1.5ms preprocess, 14.2ms inference, 2.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 13.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 40.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 3.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.0ms\n",
|
|
"Speed: 1.1ms preprocess, 15.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.0ms\n",
|
|
"Speed: 1.8ms preprocess, 14.0ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 14.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 pants, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 pants, 15.9ms\n",
|
|
"Speed: 2.8ms preprocess, 15.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 7.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 25.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.1ms\n",
|
|
"Speed: 2.0ms preprocess, 14.1ms inference, 3.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 7.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 13.9ms\n",
|
|
"Speed: 2.5ms preprocess, 13.9ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 1.0ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 7.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 32.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.0ms\n",
|
|
"Speed: 2.4ms preprocess, 14.0ms inference, 7.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 7.5ms\n",
|
|
"Speed: 2.0ms preprocess, 7.5ms inference, 2.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.8ms\n",
|
|
"Speed: 2.8ms preprocess, 14.8ms inference, 2.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 13.9ms\n",
|
|
"Speed: 1.4ms preprocess, 13.9ms inference, 21.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 25.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 20.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 12.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 2.8ms preprocess, 15.9ms inference, 2.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 15.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 3.6ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 t-shirt, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 3.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 t-shirt, 9.6ms\n",
|
|
"Speed: 1.1ms preprocess, 9.6ms inference, 2.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 4.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 12.4ms\n",
|
|
"Speed: 1.0ms preprocess, 12.4ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 29.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 12.2ms\n",
|
|
"Speed: 1.9ms preprocess, 12.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.4ms\n",
|
|
"Speed: 0.9ms preprocess, 16.4ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 12.5ms\n",
|
|
"Speed: 1.2ms preprocess, 12.5ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 14.9ms\n",
|
|
"Speed: 1.7ms preprocess, 14.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 12.4ms\n",
|
|
"Speed: 2.2ms preprocess, 12.4ms inference, 7.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 7.8ms\n",
|
|
"Speed: 1.3ms preprocess, 7.8ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.3ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 28.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 9.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 35.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 23.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 24.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 6.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 32.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 26.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 17.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 15.8ms\n",
|
|
"Speed: 2.8ms preprocess, 15.8ms inference, 18.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 dress, 7.6ms\n",
|
|
"Speed: 1.4ms preprocess, 7.6ms inference, 2.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 15.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 31.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 8.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 32.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 16.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 10.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 19.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 12.4ms\n",
|
|
"Speed: 1.3ms preprocess, 12.4ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 18.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 7.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 31.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 8.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 18.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 19.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.1ms preprocess, 15.9ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 18.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 10.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.5ms preprocess, 16.0ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.5ms\n",
|
|
"Speed: 2.8ms preprocess, 15.5ms inference, 3.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 5.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 15.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 11.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 16.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 5.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 3.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 15.5ms\n",
|
|
"Speed: 1.8ms preprocess, 15.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 4.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 4.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.8ms\n",
|
|
"Speed: 2.2ms preprocess, 7.8ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.0ms\n",
|
|
"Speed: 3.3ms preprocess, 16.0ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 0.9ms preprocess, 16.1ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 15.9ms\n",
|
|
"Speed: 2.0ms preprocess, 15.9ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 15.9ms\n",
|
|
"Speed: 1.2ms preprocess, 15.9ms inference, 2.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 2.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 10.7ms\n",
|
|
"Speed: 1.0ms preprocess, 10.7ms inference, 1.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 8.5ms\n",
|
|
"Speed: 1.0ms preprocess, 8.5ms inference, 1.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 7.5ms\n",
|
|
"Speed: 0.9ms preprocess, 7.5ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 2.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 7.7ms\n",
|
|
"Speed: 0.9ms preprocess, 7.7ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 8.2ms\n",
|
|
"Speed: 1.0ms preprocess, 8.2ms inference, 2.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.6ms\n",
|
|
"Speed: 1.0ms preprocess, 7.6ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 1 coat, 7.6ms\n",
|
|
"Speed: 1.0ms preprocess, 7.6ms inference, 2.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.3ms\n",
|
|
"Speed: 2.4ms preprocess, 16.3ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.9ms\n",
|
|
"Speed: 0.9ms preprocess, 7.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.7ms\n",
|
|
"Speed: 1.1ms preprocess, 7.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 0.9ms preprocess, 7.6ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.7ms\n",
|
|
"Speed: 1.2ms preprocess, 7.7ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.7ms\n",
|
|
"Speed: 1.0ms preprocess, 7.7ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 1.5ms preprocess, 7.6ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 7.8ms\n",
|
|
"Speed: 0.9ms preprocess, 7.8ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 7.6ms\n",
|
|
"Speed: 0.9ms preprocess, 7.6ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.5ms\n",
|
|
"Speed: 1.6ms preprocess, 7.5ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.7ms\n",
|
|
"Speed: 1.6ms preprocess, 7.7ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 7.7ms\n",
|
|
"Speed: 1.0ms preprocess, 7.7ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 dress, 7.6ms\n",
|
|
"Speed: 1.6ms preprocess, 7.6ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 1.1ms preprocess, 7.6ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 1.6ms preprocess, 7.6ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 0.9ms preprocess, 7.6ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.6ms\n",
|
|
"Speed: 1.5ms preprocess, 7.6ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 7.7ms\n",
|
|
"Speed: 1.1ms preprocess, 7.7ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.8ms\n",
|
|
"Speed: 1.1ms preprocess, 7.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 7.5ms\n",
|
|
"Speed: 0.9ms preprocess, 7.5ms inference, 3.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 9.7ms\n",
|
|
"Speed: 1.1ms preprocess, 9.7ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 7.1ms\n",
|
|
"Speed: 1.2ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 7.1ms\n",
|
|
"Speed: 1.1ms preprocess, 7.1ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 6.9ms\n",
|
|
"Speed: 1.0ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 7.2ms\n",
|
|
"Speed: 1.2ms preprocess, 7.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 6.9ms\n",
|
|
"Speed: 1.0ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 jacket, 1 coat, 7.3ms\n",
|
|
"Speed: 1.1ms preprocess, 7.3ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 coat, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 3.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 35.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 17.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 23.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 23.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 11.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 22.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 19.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 14.4ms\n",
|
|
"Speed: 1.2ms preprocess, 14.4ms inference, 33.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 15.9ms\n",
|
|
"Speed: 1.2ms preprocess, 15.9ms inference, 22.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 19.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 7.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 skirt, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 26.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 18.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 10.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 14.1ms\n",
|
|
"Speed: 1.7ms preprocess, 14.1ms inference, 4.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 11.2ms\n",
|
|
"Speed: 2.3ms preprocess, 11.2ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 15.9ms\n",
|
|
"Speed: 1.1ms preprocess, 15.9ms inference, 18.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 15.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 21.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 38.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 33.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 10.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 44.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 19.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 37.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 10.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 48.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 19.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 9.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 23.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 32.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 13.0ms\n",
|
|
"Speed: 2.8ms preprocess, 13.0ms inference, 4.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 15.6ms\n",
|
|
"Speed: 1.3ms preprocess, 15.6ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 11.1ms\n",
|
|
"Speed: 1.8ms preprocess, 11.1ms inference, 4.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 13.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 10.9ms\n",
|
|
"Speed: 2.6ms preprocess, 10.9ms inference, 4.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 26.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 55.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 19.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 15.4ms\n",
|
|
"Speed: 1.3ms preprocess, 15.4ms inference, 55.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 4.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 13.3ms\n",
|
|
"Speed: 1.8ms preprocess, 13.3ms inference, 5.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 15.7ms\n",
|
|
"Speed: 1.4ms preprocess, 15.7ms inference, 3.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 2 pantss, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 16.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 2 pantss, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 2 jackets, 1 pants, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 25.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 87.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 39.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 87.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 15.9ms\n",
|
|
"Speed: 3.2ms preprocess, 15.9ms inference, 39.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 73.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 12.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 75.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 33.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 14.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 2 jackets, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 43.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 27.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 42.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 6.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 42.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 45.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 7.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 27.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 21.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 44.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 19.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 14.7ms\n",
|
|
"Speed: 2.8ms preprocess, 14.7ms inference, 24.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 1 pants, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 13.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 33.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 74.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 13.4ms\n",
|
|
"Speed: 3.1ms preprocess, 13.4ms inference, 55.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 23.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 16.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 68.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 15.3ms\n",
|
|
"Speed: 3.0ms preprocess, 15.3ms inference, 4.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 13.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 6.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 35.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.4ms preprocess, 16.0ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 43.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 3.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 11.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 19.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 19.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 24.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 7.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 19.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 27.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 21.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 7.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 7.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 5.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 25.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 8.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 23.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 46.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 16.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 46.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 26.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 33.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 42.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 38.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 19.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 31.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 15.0ms\n",
|
|
"Speed: 2.7ms preprocess, 15.0ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 60.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 14.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 32.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 39.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 4.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 25.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 27.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 13.9ms\n",
|
|
"Speed: 1.9ms preprocess, 13.9ms inference, 13.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 35.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 21.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 14.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 10.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 21.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 10.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 24.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 22.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 dress, 1 bag, wallet, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 25.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 7.6ms\n",
|
|
"Speed: 0.9ms preprocess, 7.6ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 3.4ms preprocess, 16.0ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 26.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 21.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 28.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 6.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 28.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 26.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 10.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 34.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 60.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 36.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 41.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 21.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 15.9ms\n",
|
|
"Speed: 2.7ms preprocess, 15.9ms inference, 41.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 15.9ms\n",
|
|
"Speed: 1.6ms preprocess, 15.9ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 25.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 21.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 46.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 11.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 45.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 19.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 42.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 41.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 22.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 17.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 8.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 16.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 18.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 24.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 41.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 22.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 14.6ms\n",
|
|
"Speed: 1.5ms preprocess, 14.6ms inference, 53.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 1 dress, 14.5ms\n",
|
|
"Speed: 1.2ms preprocess, 14.5ms inference, 4.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 36.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 dress, 14.3ms\n",
|
|
"Speed: 2.7ms preprocess, 14.3ms inference, 4.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 90.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 87.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 12.3ms\n",
|
|
"Speed: 0.9ms preprocess, 12.3ms inference, 54.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 15.4ms\n",
|
|
"Speed: 1.5ms preprocess, 15.4ms inference, 56.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 12.4ms\n",
|
|
"Speed: 2.3ms preprocess, 12.4ms inference, 57.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 46.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 42.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 shirt (blouse)s, 1 jacket, 1 coat, 1 dress, 13.8ms\n",
|
|
"Speed: 1.8ms preprocess, 13.8ms inference, 51.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.2ms\n",
|
|
"Speed: 3.3ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 26.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 10.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 1 dress, 10.3ms\n",
|
|
"Speed: 1.2ms preprocess, 10.3ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 32.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 16.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 10.9ms\n",
|
|
"Speed: 2.2ms preprocess, 10.9ms inference, 4.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 coat, 1 dress, 12.3ms\n",
|
|
"Speed: 1.5ms preprocess, 12.3ms inference, 56.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 13.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 coats, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 75.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 18.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 coats, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 22.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 10.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 2 coats, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 14.3ms\n",
|
|
"Speed: 1.8ms preprocess, 14.3ms inference, 55.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 2 coats, 15.9ms\n",
|
|
"Speed: 2.6ms preprocess, 15.9ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 2 coats, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 9.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 2 coats, 1 bag, wallet, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 8.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 jackets, 1 coat, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 1 coat, 1 bag, wallet, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 33.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 jackets, 1 coat, 1 bag, wallet, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 19.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 jackets, 2 coats, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 40.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 30.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 12.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 45.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 3.7ms preprocess, 16.0ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 dress, 8.7ms\n",
|
|
"Speed: 1.5ms preprocess, 8.7ms inference, 4.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 13.2ms\n",
|
|
"Speed: 2.8ms preprocess, 13.2ms inference, 56.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 60.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 coats, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 25.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 87.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 2 coats, 13.9ms\n",
|
|
"Speed: 1.1ms preprocess, 13.9ms inference, 50.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 16.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 23.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 24.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 3.3ms preprocess, 16.0ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 28.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 15.9ms\n",
|
|
"Speed: 1.0ms preprocess, 15.9ms inference, 22.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 33.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.6ms preprocess, 16.0ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 2 coats, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 22.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 89.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 14.0ms\n",
|
|
"Speed: 1.0ms preprocess, 14.0ms inference, 50.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 2 coats, 14.7ms\n",
|
|
"Speed: 2.9ms preprocess, 14.7ms inference, 54.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 2 coats, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 2 coats, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 14.3ms\n",
|
|
"Speed: 2.5ms preprocess, 14.3ms inference, 53.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 13.1ms\n",
|
|
"Speed: 1.0ms preprocess, 13.1ms inference, 53.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 38.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 1.0ms preprocess, 16.0ms inference, 22.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 11.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 13.3ms\n",
|
|
"Speed: 2.6ms preprocess, 13.3ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 15.0ms\n",
|
|
"Speed: 2.7ms preprocess, 15.0ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 13.0ms\n",
|
|
"Speed: 0.9ms preprocess, 13.0ms inference, 51.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 24.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 10.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 15.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 8.4ms\n",
|
|
"Speed: 2.4ms preprocess, 8.4ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 12.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.0ms preprocess, 16.0ms inference, 20.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 14.2ms\n",
|
|
"Speed: 1.5ms preprocess, 14.2ms inference, 53.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 17.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 36.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 10.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 19.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 11.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 131.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 19.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 27.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 19.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 87.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 44.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 18.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 117.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 22.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 36.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 11.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 26.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 131.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 3 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 12.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 129.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 19.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 2 dresss, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 131.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 129.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 2 dresss, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 14.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 42.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 15.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.3ms\n",
|
|
"Speed: 1.7ms preprocess, 16.3ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 17.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 117.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 117.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 21.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 19.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 143.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 19.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 115.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 18.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 12.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 15.9ms\n",
|
|
"Speed: 1.4ms preprocess, 15.9ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 22.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 18.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 115.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 15.9ms\n",
|
|
"Speed: 1.2ms preprocess, 15.9ms inference, 26.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 shirt (blouse)s, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 13.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 131.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 22.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 131.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 19.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 11.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 24.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 131.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 2 pantss, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 14.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 131.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 1.0ms preprocess, 16.0ms inference, 24.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 131.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 11.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 3 pantss, 1 dress, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 150.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 18.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 3.7ms preprocess, 16.1ms inference, 131.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 14.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 23.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 117.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 10.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 24.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 15.9ms\n",
|
|
"Speed: 1.3ms preprocess, 15.9ms inference, 13.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 24.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 28.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 13.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 21.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 117.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 4.4ms preprocess, 16.2ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 30.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 9.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 1.4ms preprocess, 15.9ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 14.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 15.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 15.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 17.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 13.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 117.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 22.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 dress, 15.9ms\n",
|
|
"Speed: 2.1ms preprocess, 15.9ms inference, 104.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 15.9ms\n",
|
|
"Speed: 2.3ms preprocess, 15.9ms inference, 30.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 117.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 117.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 23.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 117.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 15.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 31.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 46.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 75.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 9.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 17.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 4.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 17.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 18.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 dress, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 dress, 15.0ms\n",
|
|
"Speed: 2.7ms preprocess, 15.0ms inference, 50.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 75.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 dress, 12.3ms\n",
|
|
"Speed: 1.7ms preprocess, 12.3ms inference, 56.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 1.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 75.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 10.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 11.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 16.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 7.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 14.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 19.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 11.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 13.8ms\n",
|
|
"Speed: 1.4ms preprocess, 13.8ms inference, 4.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 9.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.3ms preprocess, 15.9ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 26.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 17.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 4.0ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 24.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 10.1ms\n",
|
|
"Speed: 1.6ms preprocess, 10.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 26.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 46.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 4.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 14.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 14.8ms\n",
|
|
"Speed: 1.9ms preprocess, 14.8ms inference, 50.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 5.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 14.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 13.1ms\n",
|
|
"Speed: 1.4ms preprocess, 13.1ms inference, 4.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 3.9ms preprocess, 16.1ms inference, 74.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 13.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 1 coat, 15.8ms\n",
|
|
"Speed: 2.9ms preprocess, 15.8ms inference, 12.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 coat, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 12.4ms\n",
|
|
"Speed: 1.7ms preprocess, 12.4ms inference, 53.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 22.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 76.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.4ms preprocess, 15.9ms inference, 10.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.3ms\n",
|
|
"Speed: 2.3ms preprocess, 16.3ms inference, 16.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 87.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 8.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 15.9ms\n",
|
|
"Speed: 1.9ms preprocess, 15.9ms inference, 25.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 8.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 87.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 14.1ms\n",
|
|
"Speed: 1.7ms preprocess, 14.1ms inference, 4.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 15.6ms\n",
|
|
"Speed: 1.2ms preprocess, 15.6ms inference, 54.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 19.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 17.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 3.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 8.5ms\n",
|
|
"Speed: 1.5ms preprocess, 8.5ms inference, 4.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 8.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 18.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 14.4ms\n",
|
|
"Speed: 2.5ms preprocess, 14.4ms inference, 55.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 90.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 12.0ms\n",
|
|
"Speed: 1.0ms preprocess, 12.0ms inference, 57.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 13.9ms\n",
|
|
"Speed: 1.1ms preprocess, 13.9ms inference, 50.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 90.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 13.2ms\n",
|
|
"Speed: 2.0ms preprocess, 13.2ms inference, 57.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 44.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 90.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 12.4ms\n",
|
|
"Speed: 1.7ms preprocess, 12.4ms inference, 55.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 35.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 21.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 42.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.2ms preprocess, 15.9ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 9.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 15.9ms\n",
|
|
"Speed: 2.6ms preprocess, 15.9ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 13.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 22.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 23.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 16.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 27.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 25.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 33.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 103.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 29.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 104.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 18.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 29.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 21.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 36.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 101.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 15.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 39.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 29.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 16.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 26.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 27.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 89.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 28.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 37.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 33.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 13.3ms\n",
|
|
"Speed: 1.0ms preprocess, 13.3ms inference, 53.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 35.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 14.0ms\n",
|
|
"Speed: 1.0ms preprocess, 14.0ms inference, 50.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 15.4ms\n",
|
|
"Speed: 1.6ms preprocess, 15.4ms inference, 56.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 14.3ms\n",
|
|
"Speed: 2.6ms preprocess, 14.3ms inference, 4.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 2 dresss, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 32.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 2 dresss, 16.2ms\n",
|
|
"Speed: 2.5ms preprocess, 16.2ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 37.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 14.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 26.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 131.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 11.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 22.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 2 dresss, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 18.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 34.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 15.9ms\n",
|
|
"Speed: 2.4ms preprocess, 15.9ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 28.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 24.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 12.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 103.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 23.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 14.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 28.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 37.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 103.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 19.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 10.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 117.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 28.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 28.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 42.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 5 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.4ms preprocess, 16.1ms inference, 47.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 27.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 12.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 89.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 29.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 12.2ms\n",
|
|
"Speed: 1.7ms preprocess, 12.2ms inference, 58.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 11.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 89.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 28.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 12.5ms\n",
|
|
"Speed: 2.7ms preprocess, 12.5ms inference, 54.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 30.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 coat, 13.3ms\n",
|
|
"Speed: 2.6ms preprocess, 13.3ms inference, 48.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 2 pantss, 1 coat, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 117.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 2 pantss, 1 coat, 13.2ms\n",
|
|
"Speed: 1.7ms preprocess, 13.2ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 1 pants, 2 coats, 1 dress, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 34.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 2 pantss, 2 coats, 1 dress, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 142.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 1 dress, 14.9ms\n",
|
|
"Speed: 3.0ms preprocess, 14.9ms inference, 7.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 1 coat, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 31.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 2 pantss, 2 coats, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 117.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 pants, 2 coats, 12.8ms\n",
|
|
"Speed: 2.8ms preprocess, 12.8ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 coat, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 117.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 2 coats, 16.2ms\n",
|
|
"Speed: 2.3ms preprocess, 16.2ms inference, 20.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 43.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 14.8ms\n",
|
|
"Speed: 2.0ms preprocess, 14.8ms inference, 4.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 9.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 45.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 12.5ms\n",
|
|
"Speed: 1.0ms preprocess, 12.5ms inference, 57.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 101.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 14.1ms\n",
|
|
"Speed: 3.0ms preprocess, 14.1ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 28.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 76.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 12.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 33.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 101.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 26.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 dress, 16.2ms\n",
|
|
"Speed: 3.0ms preprocess, 16.2ms inference, 103.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 14.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 103.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 33.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 1 dress, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 14.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 2 jackets, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 104.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 27.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 pants, 1 coat, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 4 faces, 1 jacket, 1 coat, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 13.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 shirt (blouse)s, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 13.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 shirt (blouse)s, 2 jackets, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 12.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 14.2ms\n",
|
|
"Speed: 1.3ms preprocess, 14.2ms inference, 50.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 14.7ms\n",
|
|
"Speed: 3.0ms preprocess, 14.7ms inference, 4.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 89.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 15.9ms\n",
|
|
"Speed: 2.6ms preprocess, 15.9ms inference, 24.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 t-shirt, 1 jacket, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 90.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 12.7ms\n",
|
|
"Speed: 1.0ms preprocess, 12.7ms inference, 52.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 103.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 dress, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 12.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 dress, 13.8ms\n",
|
|
"Speed: 0.9ms preprocess, 13.8ms inference, 56.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 90.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 15.1ms\n",
|
|
"Speed: 2.8ms preprocess, 15.1ms inference, 53.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 9.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 17.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 89.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 32.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 23.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 24.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 10.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 17.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 18.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 21.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 9.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.5ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 29.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 15.7ms\n",
|
|
"Speed: 2.8ms preprocess, 15.7ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 46.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 15.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 48.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 15.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.2ms\n",
|
|
"Speed: 2.8ms preprocess, 16.2ms inference, 6.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 16.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 14.5ms\n",
|
|
"Speed: 2.5ms preprocess, 14.5ms inference, 54.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 14.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 12.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 dress, 14.5ms\n",
|
|
"Speed: 1.6ms preprocess, 14.5ms inference, 56.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 1 dress, 13.2ms\n",
|
|
"Speed: 1.0ms preprocess, 13.2ms inference, 53.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 3.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 10.3ms\n",
|
|
"Speed: 0.9ms preprocess, 10.3ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 52.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.8ms preprocess, 15.9ms inference, 27.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 shirt (blouse), 2 jackets, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 5.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 14.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 12.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 36.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 4.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 2 jackets, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 29.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 38.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 4.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 35.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 15.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 37.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 6.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 15.1ms\n",
|
|
"Speed: 1.8ms preprocess, 15.1ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 11.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 14.6ms\n",
|
|
"Speed: 3.0ms preprocess, 14.6ms inference, 53.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 4.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 9.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 62.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 14.5ms\n",
|
|
"Speed: 1.4ms preprocess, 14.5ms inference, 49.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 18.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 10.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 76.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 44.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 19.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 jacket, 1 coat, 16.0ms\n",
|
|
"Speed: 3.7ms preprocess, 16.0ms inference, 13.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 coat, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 17.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 6.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 coat, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 coat, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 30.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 16.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 dress, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 17.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 3 faces, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 76.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 dress, 12.2ms\n",
|
|
"Speed: 1.6ms preprocess, 12.2ms inference, 54.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 62.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 7.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 3.7ms preprocess, 16.0ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 2 jackets, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 27.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 10.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 12.8ms\n",
|
|
"Speed: 2.9ms preprocess, 12.8ms inference, 3.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 33.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 19.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 37.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 7.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 5.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 12.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 t-shirt, 1 jacket, 13.0ms\n",
|
|
"Speed: 1.7ms preprocess, 13.0ms inference, 3.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 12.1ms\n",
|
|
"Speed: 1.0ms preprocess, 12.1ms inference, 48.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 1.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 27.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 17.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 3.7ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 13.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 t-shirt, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 17.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 7.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 15.9ms\n",
|
|
"Speed: 2.7ms preprocess, 15.9ms inference, 44.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 8.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.4ms preprocess, 16.0ms inference, 19.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 16.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 5.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 dress, 7.8ms\n",
|
|
"Speed: 1.1ms preprocess, 7.8ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 12.3ms\n",
|
|
"Speed: 0.9ms preprocess, 12.3ms inference, 53.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 3.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 7.7ms\n",
|
|
"Speed: 1.0ms preprocess, 7.7ms inference, 4.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 10.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 16.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 62.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 5.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 9.9ms\n",
|
|
"Speed: 0.9ms preprocess, 9.9ms inference, 2.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 14.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 18.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 76.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 42.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 33.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.2ms preprocess, 16.2ms inference, 25.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.3ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 31.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 8.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 14.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 8.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 1 dress, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 33.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 27.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 28.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 6.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.4ms\n",
|
|
"Speed: 3.0ms preprocess, 16.4ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 27.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 16.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 15.9ms\n",
|
|
"Speed: 2.5ms preprocess, 15.9ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 27.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 6.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 32.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 28.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 30.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 8.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 31.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 18.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 13.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 7.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 16.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 27.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 62.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 7.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 62.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 17.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 8.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 27.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 5.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 12.1ms\n",
|
|
"Speed: 1.0ms preprocess, 12.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.9ms preprocess, 16.0ms inference, 42.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.5ms preprocess, 16.0ms inference, 19.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 30.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 31.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.2ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 27.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 3.4ms preprocess, 16.0ms inference, 34.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 8.6ms\n",
|
|
"Speed: 0.9ms preprocess, 8.6ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.4ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 15.8ms\n",
|
|
"Speed: 2.3ms preprocess, 15.8ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 13.9ms\n",
|
|
"Speed: 1.2ms preprocess, 13.9ms inference, 0.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 4.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 (no detections), 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 4.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 8.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 15.9ms\n",
|
|
"Speed: 2.1ms preprocess, 15.9ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 12.4ms\n",
|
|
"Speed: 0.9ms preprocess, 12.4ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 1.8ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 3.5ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.2ms\n",
|
|
"Speed: 1.0ms preprocess, 14.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 34.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 18.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 dress, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.4ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 2.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 17.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 22.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 13.9ms\n",
|
|
"Speed: 3.6ms preprocess, 13.9ms inference, 3.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 t-shirt, 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 11.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.2ms\n",
|
|
"Speed: 2.1ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.8ms\n",
|
|
"Speed: 3.0ms preprocess, 15.8ms inference, 2.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.1ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 16.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.9ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 14.2ms\n",
|
|
"Speed: 1.2ms preprocess, 14.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 21.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 2 jackets, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 46.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 17.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 21.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 6.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 21.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.0ms preprocess, 16.1ms inference, 8.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.8ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.6ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.2ms preprocess, 16.2ms inference, 19.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 5.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.0ms\n",
|
|
"Speed: 2.9ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 16.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.9ms\n",
|
|
"Speed: 3.7ms preprocess, 15.9ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.3ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 3.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 20.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.8ms preprocess, 16.0ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.9ms preprocess, 16.1ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.5ms preprocess, 16.1ms inference, 9.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.6ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.3ms preprocess, 16.1ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.1ms preprocess, 16.1ms inference, 10.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 42.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.0ms preprocess, 16.1ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.1ms preprocess, 16.1ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.8ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.4ms\n",
|
|
"Speed: 2.4ms preprocess, 16.4ms inference, 14.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.3ms preprocess, 16.2ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 24.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.4ms preprocess, 16.0ms inference, 5.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.2ms preprocess, 16.2ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 15.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.3ms\n",
|
|
"Speed: 2.0ms preprocess, 16.3ms inference, 48.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 38.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 7.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 48.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 35.3ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 17.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 48.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.7ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 3.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.9ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 3.1ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.5ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 5.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.2ms\n",
|
|
"Speed: 1.1ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 1.6ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.2ms\n",
|
|
"Speed: 3.3ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 1.3ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 3.2ms preprocess, 16.0ms inference, 5.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 jacket, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 9.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 1.4ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 1.6ms preprocess, 16.1ms inference, 48.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.6ms preprocess, 16.1ms inference, 32.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.2ms\n",
|
|
"Speed: 3.1ms preprocess, 16.2ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.1ms\n",
|
|
"Speed: 2.8ms preprocess, 16.1ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 3.9ms preprocess, 16.0ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.0ms\n",
|
|
"Speed: 3.0ms preprocess, 16.0ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 16.2ms\n",
|
|
"Speed: 1.6ms preprocess, 16.2ms inference, 31.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 3.2ms preprocess, 16.1ms inference, 48.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.4ms preprocess, 16.1ms inference, 33.2ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 2 shirt (blouse)s, 1 jacket, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 10.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.0ms preprocess, 16.2ms inference, 35.4ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.0ms\n",
|
|
"Speed: 2.3ms preprocess, 16.0ms inference, 26.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.7ms preprocess, 16.0ms inference, 6.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.4ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.7ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 15.6ms\n",
|
|
"Speed: 2.1ms preprocess, 15.6ms inference, 3.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.2ms preprocess, 16.0ms inference, 5.0ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 shirt (blouse), 1 jacket, 16.2ms\n",
|
|
"Speed: 1.9ms preprocess, 16.2ms inference, 48.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 2.5ms preprocess, 16.0ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.0ms preprocess, 16.1ms inference, 9.1ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 1.0ms preprocess, 16.2ms inference, 20.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 1 t-shirt, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 12.5ms\n",
|
|
"Speed: 2.5ms preprocess, 12.5ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.1ms preprocess, 16.1ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 20.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 27.9ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.2ms preprocess, 16.0ms inference, 7.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 1.5ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.0ms\n",
|
|
"Speed: 1.7ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 12.4ms\n",
|
|
"Speed: 0.9ms preprocess, 12.4ms inference, 34.6ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 16.1ms\n",
|
|
"Speed: 2.2ms preprocess, 16.1ms inference, 34.7ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 2 faces, 1 t-shirt, 16.0ms\n",
|
|
"Speed: 2.0ms preprocess, 16.0ms inference, 34.5ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.0ms\n",
|
|
"Speed: 1.1ms preprocess, 16.0ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"\n",
|
|
"0: 384x640 1 face, 16.2ms\n",
|
|
"Speed: 2.7ms preprocess, 16.2ms inference, 20.8ms postprocess per image at shape (1, 3, 384, 640)\n",
|
|
"🎉 Done! Saved to: /home/cuuva/다운로드/clothes_detect2.mp4\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from ultralytics import YOLO\n",
|
|
"import cv2\n",
|
|
"\n",
|
|
"# 경로 설정\n",
|
|
"# video_path = \"/home/cuuva/다운로드/face_recog.MOV\"\n",
|
|
"video_path = \"/home/cuuva/다운로드/test/vip/vip.mp4\"\n",
|
|
"model_path = \"/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.pt\"\n",
|
|
"output_path = \"/home/cuuva/다운로드/clothes_detect2.mp4\"\n",
|
|
"\n",
|
|
"# 모델 로드\n",
|
|
"model = YOLO(model_path)\n",
|
|
"\n",
|
|
"# 비디오 읽기\n",
|
|
"cap = cv2.VideoCapture(video_path)\n",
|
|
"\n",
|
|
"# 비디오 설정 정보 가져오기\n",
|
|
"width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
|
|
"height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
|
|
"fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
|
|
"\n",
|
|
"# 결과 저장용 VideoWriter\n",
|
|
"fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n",
|
|
"output_video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n",
|
|
"\n",
|
|
"print(\"▶ Processing... please wait\")\n",
|
|
"\n",
|
|
"while True:\n",
|
|
" ret, frame = cap.read()\n",
|
|
" if not ret:\n",
|
|
" break\n",
|
|
"\n",
|
|
" # YOLO inference (이미지 → 객체 탐지 → 시각화)\n",
|
|
" results = model(frame, conf=0.4)\n",
|
|
"\n",
|
|
" # results[0].plot() = 바운딩박스 시각화된 frame\n",
|
|
" annotated_frame = results[0].plot()\n",
|
|
"\n",
|
|
" # 비디오 저장\n",
|
|
" output_video.write(annotated_frame)\n",
|
|
"\n",
|
|
"# 종료\n",
|
|
"cap.release()\n",
|
|
"output_video.release()\n",
|
|
"\n",
|
|
"print(f\"🎉 Done! Saved to: {output_path}\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "df41712c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"📌 hood(클래스 13)가 포함된 라벨 파일들:\n",
|
|
"2970.txt\n",
|
|
"484.txt\n",
|
|
"24755.txt\n",
|
|
"50414.txt\n",
|
|
"34350.txt\n",
|
|
"26866.txt\n",
|
|
"49661.txt\n",
|
|
"45273.txt\n",
|
|
"85.txt\n",
|
|
"16644.txt\n",
|
|
"14946.txt\n",
|
|
"20471.txt\n",
|
|
"19878.txt\n",
|
|
"4666.txt\n",
|
|
"20151.txt\n",
|
|
"46561.txt\n",
|
|
"36785.txt\n",
|
|
"7074.txt\n",
|
|
"23698.txt\n",
|
|
"812.txt\n",
|
|
"20999.txt\n",
|
|
"24294.txt\n",
|
|
"22401.txt\n",
|
|
"28250.txt\n",
|
|
"20713.txt\n",
|
|
"41424.txt\n",
|
|
"8139.txt\n",
|
|
"4903.txt\n",
|
|
"27064.txt\n",
|
|
"1159.txt\n",
|
|
"46532.txt\n",
|
|
"49673.txt\n",
|
|
"14418.txt\n",
|
|
"24735.txt\n",
|
|
"304.txt\n",
|
|
"3306.txt\n",
|
|
"44325.txt\n",
|
|
"7842.txt\n",
|
|
"15524.txt\n",
|
|
"5113.txt\n",
|
|
"49122.txt\n",
|
|
"28603.txt\n",
|
|
"38885.txt\n",
|
|
"1852.txt\n",
|
|
"16484.txt\n",
|
|
"4202.txt\n",
|
|
"50008.txt\n",
|
|
"23430.txt\n",
|
|
"43580.txt\n",
|
|
"3799.txt\n",
|
|
"1242.txt\n",
|
|
"248.txt\n",
|
|
"36211.txt\n",
|
|
"36788.txt\n",
|
|
"22320.txt\n",
|
|
"5007.txt\n",
|
|
"7824.txt\n",
|
|
"33318.txt\n",
|
|
"35210.txt\n",
|
|
"26034.txt\n",
|
|
"9248.txt\n",
|
|
"7299.txt\n",
|
|
"4715.txt\n",
|
|
"20792.txt\n",
|
|
"6018.txt\n",
|
|
"8040.txt\n",
|
|
"24900.txt\n",
|
|
"36829.txt\n",
|
|
"8026.txt\n",
|
|
"20320.txt\n",
|
|
"40455.txt\n",
|
|
"26859.txt\n",
|
|
"41948.txt\n",
|
|
"21944.txt\n",
|
|
"3668.txt\n",
|
|
"26797.txt\n",
|
|
"29186.txt\n",
|
|
"5964.txt\n",
|
|
"11583.txt\n",
|
|
"42560.txt\n",
|
|
"36819.txt\n",
|
|
"14067.txt\n",
|
|
"17654.txt\n",
|
|
"17452.txt\n",
|
|
"26554.txt\n",
|
|
"17166.txt\n",
|
|
"29531.txt\n",
|
|
"44561.txt\n",
|
|
"1157.txt\n",
|
|
"25804.txt\n",
|
|
"25028.txt\n",
|
|
"24788.txt\n",
|
|
"35268.txt\n",
|
|
"104.txt\n",
|
|
"6655.txt\n",
|
|
"32025.txt\n",
|
|
"24167.txt\n",
|
|
"25020.txt\n",
|
|
"18385.txt\n",
|
|
"8277.txt\n",
|
|
"23128.txt\n",
|
|
"38782.txt\n",
|
|
"269.txt\n",
|
|
"4846.txt\n",
|
|
"22351.txt\n",
|
|
"467.txt\n",
|
|
"34740.txt\n",
|
|
"5947.txt\n",
|
|
"8008.txt\n",
|
|
"41155.txt\n",
|
|
"11347.txt\n",
|
|
"740.txt\n",
|
|
"4563.txt\n",
|
|
"14143.txt\n",
|
|
"45552.txt\n",
|
|
"8386.txt\n",
|
|
"47733.txt\n",
|
|
"35769.txt\n",
|
|
"553.txt\n",
|
|
"3416.txt\n",
|
|
"30877.txt\n",
|
|
"6979.txt\n",
|
|
"48456.txt\n",
|
|
"4063.txt\n",
|
|
"7235.txt\n",
|
|
"6721.txt\n",
|
|
"114.txt\n",
|
|
"7189.txt\n",
|
|
"25130.txt\n",
|
|
"31569.txt\n",
|
|
"4668.txt\n",
|
|
"48868.txt\n",
|
|
"28741.txt\n",
|
|
"4232.txt\n",
|
|
"46403.txt\n",
|
|
"24192.txt\n",
|
|
"12957.txt\n",
|
|
"35175.txt\n",
|
|
"13814.txt\n",
|
|
"27535.txt\n",
|
|
"49047.txt\n",
|
|
"3223.txt\n",
|
|
"851.txt\n",
|
|
"21254.txt\n",
|
|
"41878.txt\n",
|
|
"1893.txt\n",
|
|
"714.txt\n",
|
|
"7052.txt\n",
|
|
"18302.txt\n",
|
|
"20820.txt\n",
|
|
"4080.txt\n",
|
|
"5174.txt\n",
|
|
"\n",
|
|
"총 개수: 152 개\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import os\n",
|
|
"\n",
|
|
"label_dir = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train\"\n",
|
|
"\n",
|
|
"HOOD_CLASS_ID = \"12\" # hood 클래스 ID\n",
|
|
"\n",
|
|
"hood_files = []\n",
|
|
"\n",
|
|
"for txt_name in os.listdir(label_dir):\n",
|
|
" if not txt_name.endswith(\".txt\"):\n",
|
|
" continue\n",
|
|
"\n",
|
|
" txt_path = os.path.join(label_dir, txt_name)\n",
|
|
"\n",
|
|
" with open(txt_path, \"r\") as f:\n",
|
|
" lines = f.readlines()\n",
|
|
"\n",
|
|
" # 해당 txt파일 안에 hood 클래스가 있는지 확인\n",
|
|
" for line in lines:\n",
|
|
" class_id = line.split()[0]\n",
|
|
" if class_id == HOOD_CLASS_ID:\n",
|
|
" hood_files.append(txt_name)\n",
|
|
" break # 한 파일에 여러개 있어도 한 번만 기록\n",
|
|
"\n",
|
|
"print(\"📌 hood(클래스 13)가 포함된 라벨 파일들:\")\n",
|
|
"for name in hood_files:\n",
|
|
" print(name)\n",
|
|
"\n",
|
|
"print(f\"\\n총 개수: {len(hood_files)} 개\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "8abcc6ad",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x1000 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import cv2\n",
|
|
"import os\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"# ---- 경로 설정 ----\n",
|
|
"img_path = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/images/train/2970.jpg\"\n",
|
|
"label_path = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/labels/train/2970.txt\"\n",
|
|
"\n",
|
|
"# ---- 클래스 이름 ----\n",
|
|
"class_names = {\n",
|
|
" 0: \"shirt, blouse\",\n",
|
|
" 1: \"top, t-shirt, sweatshirt\",\n",
|
|
" 2: \"sweater\",\n",
|
|
" 3: \"cardigan\",\n",
|
|
" 4: \"jacket\",\n",
|
|
" 5: \"vest\",\n",
|
|
" 6: \"pants\",\n",
|
|
" 7: \"shorts\",\n",
|
|
" 8: \"skirt\",\n",
|
|
" 9: \"coat\",\n",
|
|
" 10: \"dress\",\n",
|
|
" 11: \"jumpsuit\",\n",
|
|
" 12: \"cape\",\n",
|
|
" 13: \"glasses\",\n",
|
|
" 14: \"hat\",\n",
|
|
" 15: \"hair accessory\",\n",
|
|
" 16: \"tie\",\n",
|
|
" 17: \"glove\",\n",
|
|
" 18: \"watch\",\n",
|
|
" 19: \"belt\",\n",
|
|
" 20: \"leg warmer\",\n",
|
|
" 21: \"tights, stockings\",\n",
|
|
" 22: \"sock\",\n",
|
|
" 23: \"shoe\",\n",
|
|
" 24: \"bag, wallet\",\n",
|
|
" 25: \"scarf\",\n",
|
|
" 26: \"umbrella\",\n",
|
|
" 27: \"hood\",\n",
|
|
" 28: \"collar\",\n",
|
|
" 29: \"lapel\",\n",
|
|
" 30: \"epaulette\",\n",
|
|
" 31: \"sleeve\",\n",
|
|
" 32: \"pocket\",\n",
|
|
" 33: \"neckline\",\n",
|
|
" 34: \"buckle\",\n",
|
|
" 35: \"zipper\",\n",
|
|
" 36: \"applique\",\n",
|
|
" 37: \"bead\",\n",
|
|
" 38: \"bow\",\n",
|
|
" 39: \"flower\",\n",
|
|
" 40: \"fringe\",\n",
|
|
" 41: \"ribbon\",\n",
|
|
" 42: \"rivet\",\n",
|
|
" 43: \"ruffle\",\n",
|
|
" 44: \"sequin\",\n",
|
|
" 45: \"tassel\",\n",
|
|
"}\n",
|
|
"\n",
|
|
"# ---- 이미지 로드 ----\n",
|
|
"img = cv2.imread(img_path)\n",
|
|
"if img is None:\n",
|
|
" raise FileNotFoundError(img_path)\n",
|
|
"\n",
|
|
"h, w, _ = img.shape\n",
|
|
"\n",
|
|
"# ---- 라벨 읽기 ----\n",
|
|
"with open(label_path, \"r\") as f:\n",
|
|
" lines = f.readlines()\n",
|
|
"\n",
|
|
"for line in lines:\n",
|
|
" cls, xc, yc, bw, bh = line.strip().split()\n",
|
|
" cls = int(cls)\n",
|
|
" xc, yc, bw, bh = map(float, [xc, yc, bw, bh])\n",
|
|
"\n",
|
|
" # YOLO → 픽셀 좌표 변환\n",
|
|
" x1 = int((xc - bw / 2) * w)\n",
|
|
" y1 = int((yc - bh / 2) * h)\n",
|
|
" x2 = int((xc + bw / 2) * w)\n",
|
|
" y2 = int((yc + bh / 2) * h)\n",
|
|
"\n",
|
|
" # 박스 & 텍스트\n",
|
|
" cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
|
|
" label = class_names.get(cls, f\"class {cls}\")\n",
|
|
" cv2.putText(img, label, (x1, y1 - 3),\n",
|
|
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
|
|
"\n",
|
|
"# ---- 주피터에서 시각화 ----\n",
|
|
"img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
|
|
"plt.figure(figsize=(10, 10))\n",
|
|
"plt.imshow(img_rgb)\n",
|
|
"plt.axis(\"off\")\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "1c4d16ee",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n",
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"Model summary (fused): 92 layers, 25,847,287 parameters, 0 gradients, 78.7 GFLOPs\n",
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"\n",
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"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 17, 8400) (148.5 MB)\n",
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"\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n",
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"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 1.8s, saved as '/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx' (98.9 MB)\n",
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"\n",
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"Export complete (2.6s)\n",
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"Results saved to \u001b[1m/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights\u001b[0m\n",
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"Predict: yolo predict task=detect model=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx imgsz=640 \n",
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"Validate: yolo val task=detect model=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx imgsz=640 data=/home/cuuva/experiment/datasets/fashionpedia_yolo/fashionpedia_custom.yaml \n",
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"Visualize: https://netron.app\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"from ultralytics import YOLO\n",
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"\n",
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"model = YOLO(\"/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.pt\")\n",
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"model.export(format=\"onnx\", imgsz=640, device=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e524a803",
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"metadata": {},
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"outputs": [],
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"source": []
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}
|
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],
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"metadata": {
|
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"kernelspec": {
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"display_name": "1stagedetect",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|