{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "263a619c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New https://pypi.org/project/ultralytics/8.3.235 available ๐Ÿ˜ƒ Update with 'pip install -U ultralytics'\n", "Ultralytics 8.3.225 ๐Ÿš€ Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n", "\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", "Overriding model.yaml nc=80 with nc=9\n", "\n", " from n params module arguments \n", " 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n", " 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] \n", " 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", " 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n", " 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", " 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] \n", " 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", " 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n", " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] \n", " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n", " 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n", " 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] \n", " 22 [15, 18, 21] 1 2119531 ultralytics.nn.modules.head.Detect [9, [128, 256, 512]] \n", "Model summary: 129 layers, 11,139,083 parameters, 11,139,067 gradients, 28.7 GFLOPs\n", "\n", "Transferred 349/355 items from pretrained weights\n", "Freezing layer 'model.22.dfl.conv.weight'\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed โœ…\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access โœ… (ping: 0.0ยฑ0.0 ms, read: 21065.6ยฑ11622.9 MB/s, size: 2073.5 KB)\n", "\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% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 80000/80000 166.9Mit/s 0.0s\n", "\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", "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for imgsz=640 at 60.0% CUDA memory utilization.\n", "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA GeForce RTX 5090) 31.36G total, 0.17G reserved, 0.11G allocated, 31.07G free\n", " Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/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", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 11139083 28.66 1.715 27.4 131.9 (1, 3, 640, 640) list\n", " 11139083 57.33 2.252 5.365 23.35 (2, 3, 640, 640) list\n", " 11139083 114.7 2.412 6.467 27.61 (4, 3, 640, 640) list\n", " 11139083 229.3 3.446 8.65 36.54 (8, 3, 640, 640) list\n", " 11139083 458.6 6.088 13.39 58.79 (16, 3, 640, 640) list\n", " 11139083 917.3 10.503 27.94 98.08 (32, 3, 640, 640) list\n", " 11139083 1835 19.724 57.47 183.7 (64, 3, 640, 640) list\n", "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 61 for CUDA:0 19.12G/31.36G (61%) โœ…\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access โœ… (ping: 0.0ยฑ0.0 ms, read: 12772.5ยฑ4201.8 MB/s, size: 3291.8 KB)\n", "\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% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 80000/80000 163.0Mit/s 0.0s\n", "\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", "\u001b[34m\u001b[1mval: \u001b[0mFast image access โœ… (ping: 0.0ยฑ0.0 ms, read: 1093.3ยฑ76.4 MB/s, size: 2293.4 KB)\n", "\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% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 10000/10000 16.2Mit/s 0.0s\n", "Plotting labels to /home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion/labels.jpg... \n", "\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", "Image sizes 640 train, 640 val\n", "Using 8 dataloader workers\n", "Logging results to \u001b[1m/home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion\u001b[0m\n", "Starting training for 200 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/200 15.4G 0.3159 0.7355 0.97 75 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.5it/s 8:41<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.9it/s 16.9s0.2s\n", " all 10000 10000 0.787 0.783 0.833 0.8\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/200 15.4G 0.3112 0.5777 0.9604 79 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:10<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.1it/s 19.8s0.2ss\n", " all 10000 10000 0.735 0.789 0.856 0.848\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/200 15.4G 0.2982 0.5394 0.9531 67 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:12<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 3.7it/s 22.1s0.3s\n", " all 10000 10000 0.842 0.85 0.912 0.905\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/200 15.4G 0.2806 0.4979 0.946 81 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:15<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.1it/s 20.0s0.2s\n", " all 10000 10000 0.88 0.877 0.934 0.932\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/200 15.4G 0.2685 0.4621 0.9404 72 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.4it/s 18.6s0.2s\n", " all 10000 10000 0.882 0.898 0.945 0.944\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/200 15.4G 0.2589 0.4416 0.9363 70 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:12<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.5it/s 18.3s0.2s\n", " all 10000 10000 0.905 0.896 0.95 0.949\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/200 15.4G 0.2526 0.4271 0.9336 67 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:10<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.7it/s 17.4s0.2s\n", " all 10000 10000 0.898 0.912 0.956 0.956\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/200 15.4G 0.249 0.4116 0.9327 80 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:11<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.7it/s 17.5s0.2s\n", " all 10000 10000 0.912 0.909 0.959 0.959\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/200 15.4G 0.2472 0.4026 0.9319 79 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:16<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.5it/s 18.3s0.2s\n", " all 10000 10000 0.904 0.92 0.96 0.96\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/200 15.4G 0.2414 0.3935 0.9288 75 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.7it/s 17.5s0.3s\n", " all 10000 10000 0.917 0.913 0.961 0.961\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/200 15.4G 0.2369 0.3808 0.927 73 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:14<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.4it/s 18.6s0.2s\n", " all 10000 10000 0.914 0.915 0.963 0.963\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/200 15.4G 0.2347 0.3767 0.9259 78 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.4it/s 18.5s0.2s\n", " all 10000 10000 0.914 0.914 0.964 0.963\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/200 15.4G 0.2325 0.3699 0.9249 86 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:15<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.5it/s 18.4s0.2s\n", " all 10000 10000 0.902 0.927 0.964 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/200 15.4G 0.2305 0.3635 0.9247 87 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 3.9it/s 21.1s0.3s\n", " all 10000 10000 0.914 0.918 0.964 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/200 15.4G 0.228 0.3587 0.9233 74 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:14<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.4it/s 18.7s0.2s\n", " all 10000 10000 0.917 0.918 0.964 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/200 15.4G 0.2273 0.3549 0.9228 69 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:12<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.3it/s 18.9s0.3s\n", " all 10000 10000 0.915 0.92 0.964 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/200 15.4G 0.2274 0.3507 0.9232 74 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:14<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.3it/s 19.2s0.3s\n", " all 10000 10000 0.918 0.917 0.965 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/200 15.4G 0.2268 0.3474 0.9235 67 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:15<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.2it/s 19.5s0.2s\n", " all 10000 10000 0.915 0.918 0.965 0.964\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/200 15.4G 0.2242 0.3439 0.9217 70 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.2it/s 19.7s0.2s\n", " all 10000 10000 0.913 0.92 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/200 15.4G 0.2234 0.3396 0.9218 87 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:16<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 3.9it/s 21.1s0.2s\n", " all 10000 10000 0.905 0.927 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 21/200 15.4G 0.2216 0.3376 0.9203 82 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:16<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.1it/s 19.8s0.2s\n", " all 10000 10000 0.909 0.922 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 22/200 15.4G 0.2208 0.3317 0.9208 76 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:16<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.1it/s 20.1s0.3s\n", " all 10000 10000 0.909 0.924 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 23/200 15.4G 0.221 0.3307 0.9208 76 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.6it/s 8:16<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.0it/s 20.3s0.3s\n", " all 10000 10000 0.907 0.925 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 24/200 15.4G inf 0.3275 0.9202 69 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:14<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.0it/s 20.3s0.3s\n", " all 10000 10000 0.909 0.923 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 25/200 15.4G 0.2193 0.3237 0.9201 77 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:13<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 3.9it/s 21.2s0.3s\n", " all 10000 10000 0.917 0.918 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 26/200 15.4G 0.2178 0.3203 0.9188 74 640: 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1312/1312 2.7it/s 8:14<0.2s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 82/82 4.3it/s 19.3s0.3s\n", " all 10000 10000 0.916 0.919 0.965 0.965\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 27/200 15.4G 0.2203 0.3182 0.9208 151 640: 22% โ”โ”โ•ธโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ 287/1312 3.6it/s 1:59<4:438\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "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. ํ•™์Šต ์‹คํ–‰\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# ์œ„์—์„œ ์ƒ์„ฑ๋œ yaml ํŒŒ์ผ ๊ฒฝ๋กœ๋ฅผ ๋„ฃ์–ด์ค๋‹ˆ๋‹ค.\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# Fashionpedia๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋งŽ๊ณ  ๋ณต์žกํ•ด์„œ epoch์„ ์กฐ์ ˆํ•ด์•ผ ํ•  ์ˆ˜๋„ ์žˆ์–ด\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 ๋ฉ”๋ชจ๋ฆฌ์— ๋งž์ถฐ ์กฐ์ ˆ (-1์€ 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", "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", "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", "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", "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", "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\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. ๋ชจ๋ธ ๋กœ๋“œ (YOLOv8m ์‚ฌ์šฉ)\n", "model = YOLO('yolov8s.pt')\n", "\n", "# 2. ํ•™์Šต ์‹คํ–‰\n", "# ์œ„์—์„œ ์ƒ์„ฑ๋œ yaml ํŒŒ์ผ ๊ฒฝ๋กœ๋ฅผ ๋„ฃ์–ด์ค๋‹ˆ๋‹ค.\n", "train_results = model.train(\n", " data=\"/home/cuuva/experiment/fashion_yolo/aihub_fashion.yaml\", \n", " epochs=200, # Fashionpedia๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋งŽ๊ณ  ๋ณต์žกํ•ด์„œ epoch์„ ์กฐ์ ˆํ•ด์•ผ ํ•  ์ˆ˜๋„ ์žˆ์–ด\n", " imgsz=640,\n", " batch=-1, # GPU ๋ฉ”๋ชจ๋ฆฌ์— ๋งž์ถฐ ์กฐ์ ˆ (-1์€ 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": [ "๐Ÿ”„ Inference ์‹œ์ž‘...\n", "๐ŸŽ‰ ์™„๋ฃŒ! ์ €์žฅ ์œ„์น˜: /home/cuuva/๋‹ค์šด๋กœ๋“œ/infer_clothes.mp4\n" ] } ], "source": [ "from ultralytics import YOLO\n", "import cv2\n", "\n", "# ---- ๊ฒฝ๋กœ ์„ค์ • ----\n", "model_path = \"/home/cuuva/experiment/fashion_yolo/aihub_exp/yolov8s_fashion/weights/best.pt\"\n", "input_video_path = \"/home/cuuva/๋‹ค์šด๋กœ๋“œ/face_recog.MOV\"\n", "output_video_path = \"/home/cuuva/๋‹ค์šด๋กœ๋“œ/infer_clothes.mp4\"\n", "\n", "# ---- ๋ชจ๋ธ ๋กœ๋“œ ----\n", "model = YOLO(model_path)\n", "\n", "# ---- ์ž…๋ ฅ ์˜์ƒ ๋กœ๋“œ ----\n", "cap = cv2.VideoCapture(input_video_path)\n", "\n", "# ์˜์ƒ ์ •๋ณด ์ฝ๊ธฐ\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", "# ---- ์ถœ๋ ฅ ์˜์ƒ ์ €์žฅ ์„ค์ • ----\n", "fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n", "out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))\n", "\n", "print(f\"๐Ÿ”„ Inference ์‹œ์ž‘...\")\n", "\n", "while cap.isOpened():\n", " ret, frame = cap.read()\n", " if not ret:\n", " break\n", "\n", " # ---- YOLO ์ถ”๋ก  ----\n", " results = model(frame, verbose=False)\n", "\n", " # YOLO ๊ฒฐ๊ณผ ์ด๋ฏธ์ง€ ๋ Œ๋”๋ง\n", " annotated_frame = results[0].plot()\n", "\n", " # ---- ์˜์ƒ ์ €์žฅ ----\n", " out.write(annotated_frame)\n", "\n", "# ---- ์ž์› ์ •๋ฆฌ ----\n", "cap.release()\n", "out.release()\n", "\n", "print(f\"๐ŸŽ‰ ์™„๋ฃŒ! ์ €์žฅ ์œ„์น˜: {output_video_path}\")\n" ] }, { "cell_type": "markdown", "id": "f338c8c1", "metadata": {}, "source": [ "# AIHUB๋กœ Inferenceํ•˜๋ฉด ์„ฑ๋Šฅ ๊ฐœ์ฐจ๋ฐ˜. " ] }, { "cell_type": "code", "execution_count": 4, "id": "e8a7e835", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” ์ด๋ฏธ์ง€ ๊ฒ€์‚ฌ ์ค‘...\n", "๐Ÿ“Œ ์กฐ๊ฑด ์ถฉ์กฑ ์ด๋ฏธ์ง€ ๊ฐœ์ˆ˜: 45533 ๊ฐœ\n", "๐Ÿ“ฆ ์ตœ์ข… ๋ณต์‚ฌํ•  ์ด๋ฏธ์ง€ ์ˆ˜: 300\n", "\n", "๐ŸŽ‰ ์™„๋ฃŒ! ์–‘์žํ™”์šฉ ๋ฐ์ดํ„ฐ๊ฐ€ ์ค€๋น„๋จ.\n", "๐Ÿ“ ์ €์žฅ ์œ„์น˜ โ†’ /home/cuuva/๋‹ค์šด๋กœ๋“œ/quan_fashion_face/external1\n" ] } ], "source": [ "import os\n", "import random\n", "import shutil\n", "from PIL import Image\n", "\n", "# ---- ๊ฒฝ๋กœ ์„ค์ • ----\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/๋‹ค์šด๋กœ๋“œ/quan_fashion_face/external1\"\n", "os.makedirs(output_dir, exist_ok=True)\n", "\n", "# ---- ํ•„ํ„ฐ ๊ธฐ์ค€ ----\n", "MIN_RESOLUTION = 300 # ์ตœ์†Œ ํ•ด์ƒ๋„ ์กฐ๊ฑด\n", "REQUIRED_COUNT = 300 # ์ตœ์ข… ๋ณต์‚ฌ ๊ฐœ์ˆ˜\n", "\n", "valid_images = []\n", "\n", "print(\"๐Ÿ” ์ด๋ฏธ์ง€ ๊ฒ€์‚ฌ ์ค‘...\")\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) ๋ผ๋ฒจ ํŒŒ์ผ ์กด์žฌํ•˜๋Š”์ง€\n", " if not os.path.exists(label_path):\n", " continue\n", "\n", " # 2) ๋ผ๋ฒจ์— ๊ฐ์ฒด๊ฐ€ 1๊ฐœ ์ด์ƒ์ธ์ง€\n", " with open(label_path, \"r\") as f:\n", " lines = f.readlines()\n", " if len(lines) == 0:\n", " continue\n", "\n", " # 3) ํ•ด์ƒ๋„ ์ฒดํฌ\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\"๐Ÿ“Œ ์กฐ๊ฑด ์ถฉ์กฑ ์ด๋ฏธ์ง€ ๊ฐœ์ˆ˜: {len(valid_images)} ๊ฐœ\")\n", "\n", "# ---- 300์žฅ ์ƒ˜ํ”Œ๋ง ----\n", "if len(valid_images) < REQUIRED_COUNT:\n", " print(f\"โš ๏ธ ์กฐ๊ฑด ๋งž๋Š” ์ด๋ฏธ์ง€๊ฐ€ {REQUIRED_COUNT}๊ฐœ๋ณด๋‹ค ์ ์Šต๋‹ˆ๋‹ค. ({len(valid_images)}๊ฐœ)\")\n", " sample_images = valid_images # ๊ฐ€๋Šฅํ•œ ๋งŒํผ๋งŒ ๋ณต์‚ฌ\n", "else:\n", " sample_images = random.sample(valid_images, REQUIRED_COUNT)\n", "\n", "print(f\"๐Ÿ“ฆ ์ตœ์ข… ๋ณต์‚ฌํ•  ์ด๋ฏธ์ง€ ์ˆ˜: {len(sample_images)}\")\n", "\n", "# ---- ๋ณต์‚ฌ ์‹คํ–‰ ----\n", "for img_path in sample_images:\n", " shutil.copy(img_path, output_dir)\n", "\n", "print(\"\\n๐ŸŽ‰ ์™„๋ฃŒ! ์–‘์žํ™”์šฉ ๋ฐ์ดํ„ฐ๊ฐ€ ์ค€๋น„๋จ.\")\n", "print(f\"๐Ÿ“ ์ €์žฅ ์œ„์น˜ โ†’ {output_dir}\")\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "08f995f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” ์ด๋ฏธ์ง€ ๊ฒ€์‚ฌ ์ค‘...\n", "\n", "๐Ÿ“Œ ๊ฐ€๋กœ(W>H) ์ด๋ฏธ์ง€: 5263๊ฐœ\n", "๐Ÿ“Œ ์„ธ๋กœ(H>W) ์ด๋ฏธ์ง€: 40341๊ฐœ\n", "\n", "๐ŸŽฏ ๋ชฉํ‘œ ๋น„์œจ โ†’ Wide: 300์žฅ / Tall: 0์žฅ\n", "\n", "๐Ÿ“ฆ ์ตœ์ข… ์„ ํƒ๋œ ์ด๋ฏธ์ง€ ์ˆ˜: 300\n", "\n", "๐ŸŽ‰ ์™„๋ฃŒ! ๋น„์œจ ์กฐ์ •๋œ ์–‘์žํ™”์šฉ ์ด๋ฏธ์ง€์…‹ ์ƒ์„ฑ๋จ!\n", "๐Ÿ“ ์ €์žฅ ์œ„์น˜ โ†’ /home/cuuva/๋‹ค์šด๋กœ๋“œ/quan_fashion_face/external1\n" ] } ], "source": [ "import os\n", "import random\n", "import shutil\n", "from PIL import Image\n", "\n", "# ---- ๊ฒฝ๋กœ ์„ค์ • ----\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/๋‹ค์šด๋กœ๋“œ/quan_fashion_face/external1\"\n", "os.makedirs(output_dir, exist_ok=True)\n", "\n", "# ---- ํ•„ํ„ฐ ๊ธฐ์ค€ ----\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(\"๐Ÿ” ์ด๋ฏธ์ง€ ๊ฒ€์‚ฌ ์ค‘...\")\n", "\n", "# ---- ์ด๋ฏธ์ง€์™€ ๋ผ๋ฒจ ๊ฒ€์ฆ ----\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", " # ---- ๊ฐ€๋กœ/์„ธ๋กœ ๋น„์œจ ๋ถ„๋ฅ˜ ----\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\"โš ๏ธ ์˜ค๋ฅ˜ ๋ฐœ์ƒ: {e}\")\n", " continue\n", "\n", "print(f\"\\n๐Ÿ“Œ ๊ฐ€๋กœ(W>H) ์ด๋ฏธ์ง€: {len(valid_wide_images)}๊ฐœ\")\n", "print(f\"๐Ÿ“Œ ์„ธ๋กœ(H>W) ์ด๋ฏธ์ง€: {len(valid_tall_images)}๊ฐœ\")\n", "\n", "# ---- ๋ชฉํ‘œ ๊ฐœ์ˆ˜ ๊ณ„์‚ฐ ----\n", "target_wide = int(REQUIRED_COUNT * RATIO_TARGET_WIDE)\n", "target_tall = REQUIRED_COUNT - target_wide\n", "\n", "print(f\"\\n๐ŸŽฏ ๋ชฉํ‘œ ๋น„์œจ โ†’ Wide: {target_wide}์žฅ / Tall: {target_tall}์žฅ\")\n", "\n", "# ๋ถ€์กฑํ•˜๋ฉด ๊ฐ€๋Šฅํ•œ ๋งŒํผ๋งŒ\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", "# ๋งŒ์•ฝ ์–ด๋Š ํ•œ์ชฝ์ด ๋ถ€์กฑํ•˜๋ฉด ๋‹ค๋ฅธ์ชฝ์—์„œ ์ฑ„์šฐ๊ธฐ\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\"\\n๐Ÿ“ฆ ์ตœ์ข… ์„ ํƒ๋œ ์ด๋ฏธ์ง€ ์ˆ˜: {len(selected_images)}\")\n", "\n", "# ---- ๋ณต์‚ฌ ์‹คํ–‰ ----\n", "for img_path in selected_images:\n", " shutil.copy(img_path, output_dir)\n", "\n", "print(\"\\n๐ŸŽ‰ ์™„๋ฃŒ! ๋น„์œจ ์กฐ์ •๋œ ์–‘์žํ™”์šฉ ์ด๋ฏธ์ง€์…‹ ์ƒ์„ฑ๋จ!\")\n", "print(f\"๐Ÿ“ ์ €์žฅ ์œ„์น˜ โ†’ {output_dir}\")\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ac390117", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ”ง ์ด๋ฏธ์ง€ ๋ฆฌ์‚ฌ์ด์ฆˆ ์ค‘...\n", "\n", "๐ŸŽ‰ ์™„๋ฃŒ! ์ด 300๊ฐœ์˜ ์ด๋ฏธ์ง€๋ฅผ 640x384 ๋กœ ๋ณ€ํ™˜ํ–ˆ์Šต๋‹ˆ๋‹ค.\n" ] } ], "source": [ "from PIL import Image\n", "import os\n", "\n", "# ๋ฆฌ์‚ฌ์ด์ฆˆ ๋Œ€์ƒ ํด๋”\n", "input_dir = \"/home/cuuva/๋‹ค์šด๋กœ๋“œ/quan_fashion_face/external1\"\n", "\n", "# ์›ํ•˜๋Š” ์ถœ๋ ฅ ํฌ๊ธฐ\n", "TARGET_SIZE = (640, 384) # (width, height)\n", "\n", "print(\"๐Ÿ”ง ์ด๋ฏธ์ง€ ๋ฆฌ์‚ฌ์ด์ฆˆ ์ค‘...\")\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", " # ๋ฆฌ์‚ฌ์ด์ฆˆ (BILINEAR ๋ฐฉ์‹์ด ๊ฐ€์žฅ ๋ฌด๋‚œํ•จ)\n", " resized_img = img.resize(TARGET_SIZE, Image.BILINEAR)\n", "\n", " # ๋ฎ์–ด์“ฐ๊ธฐ ์ €์žฅ\n", " resized_img.save(img_path, quality=95)\n", "\n", " count += 1\n", " except Exception as e:\n", " print(f\"โš ๏ธ {filename} ์ฒ˜๋ฆฌ ์ค‘ ์˜ค๋ฅ˜: {e}\")\n", "\n", "print(f\"\\n๐ŸŽ‰ ์™„๋ฃŒ! ์ด {count}๊ฐœ์˜ ์ด๋ฏธ์ง€๋ฅผ {TARGET_SIZE[0]}x{TARGET_SIZE[1]} ๋กœ ๋ณ€ํ™˜ํ–ˆ์Šต๋‹ˆ๋‹ค.\")\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "2f4f5747", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โ–ถ 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", "๐ŸŽ‰ Done! Saved to: /home/cuuva/๋‹ค์šด๋กœ๋“œ/clothes_detect2.mp4\n" ] } ], "source": [ "from ultralytics import YOLO\n", "import cv2\n", "\n", "# ๊ฒฝ๋กœ ์„ค์ •\n", "# video_path = \"/home/cuuva/๋‹ค์šด๋กœ๋“œ/face_recog.MOV\"\n", "video_path = \"/home/cuuva/๋‹ค์šด๋กœ๋“œ/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/๋‹ค์šด๋กœ๋“œ/clothes_detect2.mp4\"\n", "\n", "# ๋ชจ๋ธ ๋กœ๋“œ\n", "model = YOLO(model_path)\n", "\n", "# ๋น„๋””์˜ค ์ฝ๊ธฐ\n", "cap = cv2.VideoCapture(video_path)\n", "\n", "# ๋น„๋””์˜ค ์„ค์ • ์ •๋ณด ๊ฐ€์ ธ์˜ค๊ธฐ\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", "# ๊ฒฐ๊ณผ ์ €์žฅ์šฉ VideoWriter\n", "fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n", "output_video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n", "\n", "print(\"โ–ถ Processing... please wait\")\n", "\n", "while True:\n", " ret, frame = cap.read()\n", " if not ret:\n", " break\n", "\n", " # YOLO inference (์ด๋ฏธ์ง€ โ†’ ๊ฐ์ฒด ํƒ์ง€ โ†’ ์‹œ๊ฐํ™”)\n", " results = model(frame, conf=0.4)\n", "\n", " # results[0].plot() = ๋ฐ”์šด๋”ฉ๋ฐ•์Šค ์‹œ๊ฐํ™”๋œ frame\n", " annotated_frame = results[0].plot()\n", "\n", " # ๋น„๋””์˜ค ์ €์žฅ\n", " output_video.write(annotated_frame)\n", "\n", "# ์ข…๋ฃŒ\n", "cap.release()\n", "output_video.release()\n", "\n", "print(f\"๐ŸŽ‰ Done! Saved to: {output_path}\")\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "df41712c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ“Œ hood(ํด๋ž˜์Šค 13)๊ฐ€ ํฌํ•จ๋œ ๋ผ๋ฒจ ํŒŒ์ผ๋“ค:\n", "17810.txt\n", "1534.txt\n", "44716.txt\n", "2437.txt\n", "27770.txt\n", "14508.txt\n", "11960.txt\n", "26759.txt\n", "42532.txt\n", "23465.txt\n", "45094.txt\n", "3062.txt\n", "3927.txt\n", "37325.txt\n", "47280.txt\n", "24939.txt\n", "47914.txt\n", "12304.txt\n", "44256.txt\n", "48797.txt\n", "35478.txt\n", "23090.txt\n", "24523.txt\n", "7199.txt\n", "11018.txt\n", "14928.txt\n", "33690.txt\n", "40848.txt\n", "34716.txt\n", "40992.txt\n", "3873.txt\n", "47158.txt\n", "43665.txt\n", "41177.txt\n", "1508.txt\n", "6271.txt\n", "3473.txt\n", "21468.txt\n", "7650.txt\n", "18289.txt\n", "39864.txt\n", "22130.txt\n", "2897.txt\n", "10908.txt\n", "47202.txt\n", "19684.txt\n", "49164.txt\n", "48473.txt\n", "25254.txt\n", "14300.txt\n", "15697.txt\n", "23235.txt\n", "25346.txt\n", "8872.txt\n", "34567.txt\n", "47064.txt\n", "46832.txt\n", "10487.txt\n", "18364.txt\n", "21077.txt\n", "22677.txt\n", "32253.txt\n", "15195.txt\n", "38001.txt\n", "45709.txt\n", "8453.txt\n", "626.txt\n", "2288.txt\n", "10343.txt\n", "19932.txt\n", "6744.txt\n", "22332.txt\n", "4681.txt\n", "25746.txt\n", "2672.txt\n", "23085.txt\n", "12339.txt\n", "3101.txt\n", "48165.txt\n", "16307.txt\n", "37760.txt\n", "3068.txt\n", "16592.txt\n", "4394.txt\n", "47276.txt\n", "167.txt\n", "44024.txt\n", "9909.txt\n", "7203.txt\n", "13591.txt\n", "38241.txt\n", "21841.txt\n", "2450.txt\n", "5558.txt\n", "21700.txt\n", "32721.txt\n", "3800.txt\n", "32704.txt\n", "1926.txt\n", "47498.txt\n", "27312.txt\n", "15423.txt\n", "31158.txt\n", "15166.txt\n", "43664.txt\n", "47561.txt\n", "13496.txt\n", "11798.txt\n", "18873.txt\n", "44605.txt\n", "41879.txt\n", "2009.txt\n", "20062.txt\n", "7655.txt\n", "24598.txt\n", "19109.txt\n", "37114.txt\n", "17585.txt\n", "1079.txt\n", "35182.txt\n", "2970.txt\n", "23782.txt\n", "10415.txt\n", "46871.txt\n", "7409.txt\n", "15647.txt\n", "1643.txt\n", "39273.txt\n", "33134.txt\n", "4686.txt\n", "6925.txt\n", "8472.txt\n", "10434.txt\n", "39859.txt\n", "19604.txt\n", "31083.txt\n", "40332.txt\n", "49081.txt\n", "505.txt\n", "25331.txt\n", "25228.txt\n", "5836.txt\n", "18146.txt\n", "30678.txt\n", "16518.txt\n", "37302.txt\n", "65.txt\n", "8497.txt\n", "16000.txt\n", "4595.txt\n", "1479.txt\n", "2831.txt\n", "22420.txt\n", "29075.txt\n", "45931.txt\n", "10932.txt\n", "47126.txt\n", "4688.txt\n", "47000.txt\n", "18173.txt\n", "18298.txt\n", "42237.txt\n", "232.txt\n", "37339.txt\n", "49629.txt\n", "17192.txt\n", "22974.txt\n", "2165.txt\n", "1523.txt\n", "27493.txt\n", "1286.txt\n", "5103.txt\n", "34074.txt\n", "14743.txt\n", "4985.txt\n", "8265.txt\n", "11590.txt\n", "20855.txt\n", "34440.txt\n", "34600.txt\n", "21100.txt\n", "41026.txt\n", "39654.txt\n", "22020.txt\n", "31097.txt\n", "29628.txt\n", "22738.txt\n", 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"6652.txt\n", "24711.txt\n", "16397.txt\n", "34486.txt\n", "31373.txt\n", "17620.txt\n", "10188.txt\n", "2179.txt\n", "14173.txt\n", "27387.txt\n", "7861.txt\n", "2950.txt\n", "11663.txt\n", "20263.txt\n", "11319.txt\n", "40.txt\n", "41755.txt\n", "5781.txt\n", "14567.txt\n", "30257.txt\n", "8551.txt\n", "14528.txt\n", "30182.txt\n", "2969.txt\n", "41142.txt\n", "20876.txt\n", "50171.txt\n", "43896.txt\n", "29765.txt\n", "4402.txt\n", "14872.txt\n", "29555.txt\n", "5375.txt\n", "26875.txt\n", "6496.txt\n", "23749.txt\n", "3770.txt\n", "38677.txt\n", "17473.txt\n", "16216.txt\n", "31542.txt\n", "\n", "์ด ๊ฐœ์ˆ˜: 23954 ๊ฐœ\n" ] } ], "source": [ "import os\n", "\n", "label_dir = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/labels_all_bak/train\"\n", "\n", "HOOD_CLASS_ID = \"23\" # hood ํด๋ž˜์Šค 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", " # ํ•ด๋‹น txtํŒŒ์ผ ์•ˆ์— hood ํด๋ž˜์Šค๊ฐ€ ์žˆ๋Š”์ง€ ํ™•์ธ\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 # ํ•œ ํŒŒ์ผ์— ์—ฌ๋Ÿฌ๊ฐœ ์žˆ์–ด๋„ ํ•œ ๋ฒˆ๋งŒ ๊ธฐ๋ก\n", "\n", "print(\"๐Ÿ“Œ hood(ํด๋ž˜์Šค 13)๊ฐ€ ํฌํ•จ๋œ ๋ผ๋ฒจ ํŒŒ์ผ๋“ค:\")\n", "for name in hood_files:\n", " print(name)\n", "\n", "print(f\"\\n์ด ๊ฐœ์ˆ˜: {len(hood_files)} ๊ฐœ\")\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "8abcc6ad", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import cv2\n", "import os\n", "import matplotlib.pyplot as plt\n", "\n", "# ---- ๊ฒฝ๋กœ ์„ค์ • ----\n", "img_path = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/images/train/27770.jpg\"\n", "label_path = \"/home/cuuva/experiment/datasets/fashionpedia_yolo/labels_all_bak/train/27770.txt\"\n", "\n", "# ---- ํด๋ž˜์Šค ์ด๋ฆ„ ----\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", "# ---- ์ด๋ฏธ์ง€ ๋กœ๋“œ ----\n", "img = cv2.imread(img_path)\n", "if img is None:\n", " raise FileNotFoundError(img_path)\n", "\n", "h, w, _ = img.shape\n", "\n", "# ---- ๋ผ๋ฒจ ์ฝ๊ธฐ ----\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 โ†’ ํ”ฝ์…€ ์ขŒํ‘œ ๋ณ€ํ™˜\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", " # ๋ฐ•์Šค & ํ…์ŠคํŠธ\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", "# ---- ์ฃผํ”ผํ„ฐ์—์„œ ์‹œ๊ฐํ™” ----\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 ๐Ÿš€ Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n", "Model summary (fused): 92 layers, 25,847,287 parameters, 0 gradients, 78.7 GFLOPs\n", "\n", "\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", "\n", "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.19.1 opset 20...\n", "\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.71...\n", "\u001b[34m\u001b[1mONNX:\u001b[0m export success โœ… 1.8s, saved as '/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx' (98.9 MB)\n", "\n", "Export complete (2.6s)\n", "Results saved to \u001b[1m/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights\u001b[0m\n", "Predict: yolo predict task=detect model=/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx imgsz=640 \n", "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", "Visualize: https://netron.app\n" ] }, { "data": { "text/plain": [ "'/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.onnx'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ultralytics import YOLO\n", "\n", "model = YOLO(\"/home/cuuva/experiment/fashion_yolo/fashionpedia_exp/yolov8m_fashion+face_nohood/weights/best.pt\")\n", "model.export(format=\"onnx\", imgsz=640, device=0)" ] }, { "cell_type": "code", "execution_count": null, "id": "e524a803", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "1stagedetect", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }