{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6bb38c03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New https://pypi.org/project/ultralytics/8.3.235 available 😃 Update with 'pip install -U ultralytics'\n", "Ultralytics 8.3.225 🚀 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/vis6class_exp/vis6class.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=600, 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=yolov8m.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=6class_final, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=vis6class_v8m, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final, 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=6\n", "\n", " from n params module arguments \n", " 0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2] \n", " 1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2] \n", " 2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True] \n", " 3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] \n", " 4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True] \n", " 5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2] \n", " 6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True] \n", " 7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2] \n", " 8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True] \n", " 9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 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 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2] \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 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2] \n", " 16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2] \n", " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2] \n", " 19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] \n", " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] \n", " 22 [15, 18, 21] 1 3779170 ultralytics.nn.modules.head.Detect [6, [192, 384, 576]] \n", "Model summary: 169 layers, 25,859,794 parameters, 25,859,778 gradients, 79.1 GFLOPs\n", "\n", "Transferred 469/475 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 ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 584.8±216.0 MB/s, size: 260.7 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/VisDrone/labels/train... 6471 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6471/6471 5.8Kit/s 1.1s0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/VisDrone/labels/train.cache\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, 0.25G reserved, 0.24G allocated, 30.87G 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": [ " 25859794 79.08 2.384 33.48 167.9 (1, 3, 640, 640) list\n", " 25859794 158.2 3.815 10.1 34.34 (2, 3, 640, 640) list\n", " 25859794 316.3 5.071 13.67 46.59 (4, 3, 640, 640) list\n", " 25859794 632.7 8.582 27.02 70.06 (8, 3, 640, 640) list\n", " 25859794 1265 12.438 27.33 117.6 (16, 3, 640, 640) list\n", " 25859794 2531 24.390 55.53 242.4 (32, 3, 640, 640) list\n", " 25859794 5061 44.548 118 448.1 (64, 3, 640, 640) list\n", "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 24 for CUDA:0 19.20G/31.36G (61%) ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 786.9±193.0 MB/s, size: 235.0 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/cuuva/experiment/datasets/VisDrone/labels/train.cache... 6471 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6471/6471 18.8Mit/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/home/cuuva/experiment/datasets/VisDrone/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed\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 ✅ (ping: 0.0±0.0 ms, read: 485.4±68.5 MB/s, size: 153.1 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /home/cuuva/experiment/datasets/VisDrone/labels/val... 548 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 548/548 3.6Kit/s 0.2s0.2s\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/cuuva/experiment/datasets/VisDrone/labels/val.cache\n", "Plotting labels to /home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/labels.jpg... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005625000000000001), 83 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/vis6class_exp/vis6class_v8m/6class_final\u001b[0m\n", "Starting training for 600 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/600 12.9G 1.445 1.249 0.9665 1365 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.1it/s 1.3s0.1s\n", " all 548 35895 0.463 0.31 0.323 0.196\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/600 14.9G 1.385 1.06 0.95 1219 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.453 0.355 0.354 0.21\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/600 14.9G 1.419 1.068 0.956 767 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.0it/s 1.3s0.1s\n", " all 548 35895 0.515 0.367 0.384 0.227\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/600 17.5G 1.362 1 0.9433 1594 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 8.8it/s 1.4s0.1s\n", " all 548 35895 0.525 0.389 0.408 0.241\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/600 15.4G 1.339 0.9599 0.9365 1186 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.53 0.407 0.43 0.258\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/600 15.5G 1.315 0.9326 0.9308 751 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.569 0.416 0.446 0.271\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/600 15.5G 1.31 0.9191 0.9272 993 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.3it/s 1.3s0.1s\n", " all 548 35895 0.594 0.415 0.451 0.27\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/600 19.9G 1.301 0.9078 0.9265 952 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.566 0.44 0.461 0.278\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/600 16.4G 1.292 0.8996 0.9235 1348 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.3it/s 1.3s0.1s\n", " all 548 35895 0.602 0.431 0.473 0.29\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/600 13.1G 1.288 0.8853 0.9202 1021 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.604 0.427 0.469 0.287\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/600 14.6G 1.267 0.8647 0.9175 1350 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.3it/s 1.3s0.1s\n", " all 548 35895 0.626 0.436 0.486 0.296\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/600 14.7G 1.266 0.859 0.9155 1035 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.623 0.433 0.479 0.294\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/600 18G 1.261 0.8577 0.9165 1670 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.611 0.457 0.495 0.304\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/600 13.8G 1.252 0.843 0.9136 1057 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.594 0.44 0.474 0.285\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/600 15.8G 1.244 0.8313 0.9103 775 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.596 0.455 0.488 0.299\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/600 16.9G 1.237 0.8336 0.911 1047 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.596 0.459 0.49 0.3\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/600 17G 1.237 0.8241 0.9092 1032 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.612 0.455 0.498 0.31\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/600 14.2G 1.233 0.8178 0.9077 870 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.608 0.473 0.506 0.314\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/600 17.4G 1.221 0.8069 0.9055 1393 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.632 0.468 0.51 0.316\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/600 15.2G 1.219 0.7992 0.9058 1476 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.619 0.468 0.507 0.315\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 21/600 17.3G 1.229 0.8055 0.9063 758 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.618 0.481 0.516 0.321\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 22/600 16G 1.212 0.7917 0.9014 1240 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.643 0.467 0.513 0.322\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 23/600 13.1G 1.208 0.7868 0.9001 1464 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.612 0.479 0.51 0.321\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 24/600 19.1G 1.208 0.781 0.8999 1004 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.633 0.479 0.519 0.322\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 25/600 13.4G 1.198 0.7793 0.8989 1063 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.613 0.483 0.517 0.321\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 26/600 16G 1.201 0.776 0.8997 1204 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.622 0.47 0.509 0.32\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 27/600 12.7G 1.194 0.7654 0.8973 1102 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.636 0.479 0.525 0.328\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 28/600 12.8G 1.188 0.7647 0.8978 1248 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.637 0.477 0.525 0.325\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 29/600 12.8G 1.19 0.765 0.8954 1305 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.629 0.485 0.528 0.332\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 30/600 12.9G 1.182 0.7565 0.8955 1285 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.631 0.473 0.514 0.323\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 31/600 13G 1.187 0.7553 0.8932 1368 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.651 0.476 0.527 0.324\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 32/600 14.9G 1.181 0.7457 0.8929 1215 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.4it/s 1.3s0.1s\n", " all 548 35895 0.629 0.49 0.526 0.332\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 33/600 17.2G 1.17 0.7385 0.8911 1315 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.644 0.475 0.525 0.327\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 34/600 15.1G 1.17 0.7372 0.8915 1359 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.636 0.489 0.532 0.336\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 35/600 15.1G 1.175 0.7397 0.8909 1154 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.655 0.486 0.536 0.337\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 36/600 17.2G 1.17 0.733 0.8924 1306 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.651 0.481 0.532 0.334\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 37/600 16.5G 1.17 0.7319 0.8904 1424 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.646 0.498 0.539 0.337\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 38/600 13.5G 1.167 0.7294 0.8913 1004 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.647 0.488 0.534 0.335\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 39/600 19.6G 1.165 0.7281 0.89 893 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.634 0.495 0.537 0.337\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 40/600 14.3G 1.162 0.7236 0.8898 1366 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.653 0.49 0.538 0.339\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 41/600 16.1G 1.153 0.7136 0.8864 1025 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.65 0.493 0.541 0.339\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 42/600 16.6G 1.16 0.7192 0.8877 1359 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.67 0.484 0.543 0.342\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 43/600 14.2G 1.152 0.7105 0.8874 1346 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.641 0.484 0.532 0.336\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 44/600 14.3G 1.15 0.7067 0.8866 954 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.674 0.483 0.537 0.34\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 45/600 14.4G 1.147 0.7072 0.8861 1013 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.5it/s 1.3s0.1s\n", " all 548 35895 0.667 0.493 0.547 0.343\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 46/600 14.4G 1.151 0.7026 0.8862 888 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.658 0.495 0.544 0.342\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 47/600 14.5G 1.145 0.6974 0.885 965 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.653 0.494 0.539 0.34\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 48/600 14.6G 1.147 0.6988 0.8862 1478 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.658 0.492 0.541 0.343\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 49/600 14.6G 1.138 0.696 0.8841 1230 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.683 0.483 0.544 0.342\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 50/600 14.7G 1.133 0.6868 0.8832 690 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.672 0.498 0.547 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 51/600 14.8G 1.135 0.6875 0.8815 1220 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.642 0.503 0.542 0.343\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 52/600 14.8G 1.131 0.6848 0.8811 700 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.649 0.504 0.543 0.342\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 53/600 14.9G 1.133 0.6814 0.8817 1136 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.662 0.497 0.545 0.344\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 54/600 17.2G 1.129 0.6778 0.8806 1167 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.669 0.502 0.549 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 55/600 14G 1.127 0.6781 0.8817 827 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.3s0.1s\n", " all 548 35895 0.662 0.503 0.544 0.345\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 56/600 15.9G 1.127 0.6735 0.8817 1256 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.665 0.491 0.541 0.341\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 57/600 15.6G 1.124 0.668 0.8794 767 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.678 0.49 0.547 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 58/600 17.3G 1.116 0.6639 0.8777 1586 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.667 0.5 0.547 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 59/600 19.9G 1.125 0.6706 0.8819 784 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.677 0.498 0.548 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 60/600 16.8G 1.115 0.6632 0.8788 1290 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.657 0.507 0.55 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 61/600 17.8G 1.114 0.6636 0.8793 1079 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.668 0.501 0.549 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 62/600 14.4G 1.115 0.6564 0.8767 976 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.658 0.504 0.545 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 63/600 14.5G 1.121 0.6628 0.8784 1017 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.657 0.497 0.542 0.344\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 64/600 14.6G 1.112 0.6562 0.8759 837 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.67 0.499 0.546 0.346\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 65/600 14.6G 1.112 0.6578 0.8773 980 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.669 0.502 0.549 0.347\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 66/600 17.1G 1.115 0.6524 0.877 1090 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.668 0.503 0.555 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 67/600 13.5G 1.11 0.6519 0.8775 1140 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.667 0.5 0.55 0.347\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 68/600 15.8G 1.106 0.6468 0.8757 1085 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.668 0.507 0.552 0.347\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 69/600 17.4G 1.099 0.6404 0.8755 1162 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.673 0.509 0.557 0.354\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 70/600 13.2G 1.104 0.6455 0.8757 1308 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.668 0.507 0.554 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 71/600 15G 1.097 0.6395 0.8738 1268 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.68 0.507 0.557 0.354\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 72/600 17.1G 1.104 0.645 0.8737 779 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.668 0.509 0.557 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 73/600 15.4G 1.092 0.6343 0.8717 1135 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.6it/s 1.2s0.1s\n", " all 548 35895 0.673 0.506 0.553 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 74/600 15.5G 1.09 0.6299 0.8713 1067 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.672 0.502 0.553 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 75/600 15.6G 1.09 0.6297 0.8723 955 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.659 0.513 0.555 0.354\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 76/600 17.4G 1.099 0.6337 0.8742 1592 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.655 0.511 0.552 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 77/600 14.3G 1.088 0.6283 0.8702 1049 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.673 0.504 0.553 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 78/600 14.4G 1.089 0.6233 0.8707 1067 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.681 0.504 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 79/600 16.5G 1.089 0.6245 0.8701 950 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.683 0.503 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 80/600 16.8G 1.083 0.6177 0.8696 1343 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.673 0.504 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 81/600 14.2G 1.08 0.6167 0.8701 1111 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.675 0.514 0.556 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 82/600 17.8G 1.074 0.6123 0.869 1091 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.67 0.512 0.554 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 83/600 13.7G 1.091 0.625 0.8707 796 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.671 0.514 0.554 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 84/600 17.7G 1.083 0.6186 0.8692 1187 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.67 0.515 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 85/600 12.4G 1.07 0.6098 0.8688 1280 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.67 0.507 0.553 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 86/600 14.5G 1.077 0.6106 0.8685 1639 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.659 0.518 0.553 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 87/600 18.6G 1.075 0.6126 0.8684 1285 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.665 0.514 0.553 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 88/600 13.9G 1.069 0.6067 0.8675 783 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.674 0.504 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 89/600 13.9G 1.074 0.6084 0.8685 831 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.672 0.502 0.55 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 90/600 15.8G 1.07 0.6062 0.8688 1099 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.657 0.511 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 91/600 16.1G 1.076 0.6087 0.8681 1270 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.676 0.507 0.551 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 92/600 17.8G 1.072 0.6045 0.8665 926 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.671 0.507 0.552 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 93/600 17.9G 1.069 0.6032 0.8669 833 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.661 0.51 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 94/600 18.5G 1.069 0.5982 0.8659 1440 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.683 0.499 0.551 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 95/600 16.4G 1.059 0.5972 0.8664 1395 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.685 0.501 0.553 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 96/600 14.2G 1.059 0.5929 0.8642 1213 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.667 0.51 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 97/600 16.6G 1.063 0.5937 0.8645 1007 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.667 0.507 0.553 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 98/600 14.3G 1.061 0.593 0.8651 1112 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.509 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 99/600 14.4G 1.061 0.5923 0.8646 1187 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.666 0.512 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 100/600 14.5G 1.057 0.592 0.8654 1449 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.67 0.511 0.554 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 101/600 14.5G 1.058 0.5921 0.865 1107 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.677 0.506 0.554 0.353\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 102/600 14.6G 1.059 0.5903 0.8644 1019 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.681 0.508 0.555 0.353\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 103/600 18.1G 1.049 0.5838 0.8622 740 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.673 0.512 0.557 0.354\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 104/600 16.7G 1.055 0.5867 0.8631 1042 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.687 0.51 0.557 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 105/600 13.9G 1.056 0.5868 0.863 1037 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.689 0.503 0.553 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 106/600 16.6G 1.05 0.5839 0.8616 1024 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.675 0.513 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 107/600 15.3G 1.043 0.5786 0.8636 940 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.679 0.512 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 108/600 15.4G 1.047 0.5795 0.8625 1278 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.673 0.516 0.556 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 109/600 17.5G 1.048 0.5805 0.8625 975 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.65 0.523 0.554 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 110/600 15.2G 1.044 0.5779 0.8622 1394 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.667 0.516 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 111/600 16.8G 1.041 0.572 0.8608 1364 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.681 0.513 0.556 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 112/600 14.9G 1.042 0.5767 0.8611 852 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.52 0.557 0.354\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 113/600 14.9G 1.039 0.573 0.86 931 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.664 0.518 0.557 0.353\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 114/600 17.1G 1.031 0.5682 0.8588 1510 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.7it/s 1.2s0.1s\n", " all 548 35895 0.671 0.518 0.557 0.353\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 115/600 15.9G 1.036 0.5657 0.8579 1090 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.679 0.511 0.556 0.353\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 116/600 15.4G 1.033 0.5674 0.8611 1161 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.671 0.514 0.555 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 117/600 15.4G 1.033 0.5675 0.8601 1196 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.678 0.514 0.555 0.352\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 118/600 17.7G 1.034 0.5692 0.8586 914 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.52 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 119/600 15G 1.034 0.5689 0.8596 1196 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.67 0.517 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 120/600 15.1G 1.023 0.5595 0.8596 885 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.672 0.512 0.553 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 121/600 17.5G 1.035 0.5655 0.859 946 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.516 0.553 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 122/600 15.6G 1.024 0.5604 0.8584 1107 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.664 0.515 0.552 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 123/600 20.1G 1.028 0.5634 0.8586 1141 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.66 0.516 0.553 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 124/600 16G 1.03 0.5634 0.8568 895 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.657 0.519 0.554 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 125/600 17.3G 1.027 0.5636 0.8583 1378 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.659 0.518 0.554 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 126/600 13.5G 1.03 0.5607 0.8585 845 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.663 0.517 0.553 0.351\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 127/600 15.6G 1.024 0.5568 0.8547 959 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.661 0.519 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 128/600 15.7G 1.017 0.5538 0.8561 1250 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.516 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 129/600 19.1G 1.023 0.556 0.8561 1186 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.671 0.516 0.552 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 130/600 15.3G 1.026 0.5578 0.8568 1097 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.666 0.517 0.552 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 131/600 17.5G 1.016 0.555 0.8562 1127 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.666 0.514 0.552 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 132/600 13.6G 1.015 0.5511 0.8563 1301 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.669 0.514 0.552 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 133/600 13.6G 1.012 0.5497 0.8545 1097 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.671 0.514 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 134/600 15.8G 1.017 0.5527 0.8558 1109 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:03<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.682 0.506 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 135/600 14.8G 1.014 0.5505 0.8539 1206 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.678 0.509 0.552 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 136/600 17.1G 1.013 0.5495 0.8544 632 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.682 0.505 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 137/600 17.2G 1.013 0.5474 0.8546 805 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.673 0.51 0.552 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 138/600 13.9G 1.013 0.5479 0.8552 950 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.678 0.508 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 139/600 14G 1.007 0.5465 0.8546 981 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.683 0.507 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 140/600 14.1G 1.006 0.5472 0.8535 990 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.686 0.506 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 141/600 14.1G 1.012 0.545 0.8556 868 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.688 0.505 0.552 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 142/600 16G 1.01 0.5461 0.855 1128 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.683 0.508 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 143/600 17.1G 1.005 0.5441 0.8527 1591 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.672 0.512 0.551 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 144/600 17.2G 1.003 0.5409 0.8531 1097 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.664 0.516 0.552 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 145/600 18.5G 1.004 0.5435 0.8533 1450 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.515 0.552 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 146/600 16G 1.006 0.5427 0.8532 1057 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.662 0.519 0.552 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 147/600 13.6G 0.9988 0.5389 0.8536 1095 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.659 0.52 0.552 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 148/600 13.6G 0.9958 0.5383 0.8524 894 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.661 0.517 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 149/600 16.9G 1.007 0.5423 0.8521 1010 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.662 0.514 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 150/600 15.6G 1.003 0.5375 0.8518 1044 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.514 0.551 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 151/600 15.7G 0.9982 0.5382 0.8503 1314 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.515 0.551 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 152/600 13.8G 0.9952 0.5357 0.8523 853 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.665 0.516 0.552 0.35\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 153/600 15.3G 0.9961 0.5356 0.8525 1005 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.667 0.514 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 154/600 20.3G 0.9862 0.5288 0.8487 1612 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.672 0.513 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 155/600 11.7G 0.9913 0.5326 0.8506 1078 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.67 0.513 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 156/600 19G 0.9916 0.5314 0.851 916 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.672 0.514 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 157/600 16.2G 0.9902 0.5309 0.8512 893 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.675 0.511 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 158/600 15.7G 0.9898 0.5326 0.8512 1225 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.676 0.51 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 159/600 13.3G 0.9887 0.5331 0.85 1513 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.675 0.509 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 160/600 13.4G 0.992 0.5297 0.8517 1049 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.676 0.508 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 161/600 15.6G 0.9868 0.5293 0.8501 1444 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.676 0.507 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 162/600 15.6G 0.9866 0.5283 0.8481 1337 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.675 0.509 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 163/600 15.7G 0.9826 0.5255 0.8492 1309 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.676 0.508 0.551 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 164/600 17.7G 0.9862 0.5253 0.8497 1081 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.675 0.51 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 165/600 15.8G 0.9842 0.5259 0.8499 1347 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.672 0.513 0.549 0.347\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 166/600 15.7G 0.9807 0.5235 0.8492 1067 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.674 0.512 0.549 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 167/600 17.1G 0.978 0.5218 0.8485 1564 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.676 0.511 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 168/600 14.3G 0.989 0.5297 0.8484 1215 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.675 0.512 0.55 0.348\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 169/600 15.9G 0.9752 0.5185 0.8466 910 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.673 0.512 0.55 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 170/600 12.7G 0.9809 0.5221 0.8475 1056 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.8it/s 1.2s0.1s\n", " all 548 35895 0.674 0.511 0.55 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 171/600 17.1G 0.9801 0.5243 0.8488 1475 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.673 0.511 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 172/600 17.2G 0.9747 0.5203 0.8484 1145 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.673 0.511 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 173/600 13.2G 0.9762 0.5198 0.848 1238 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.675 0.512 0.551 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 174/600 16.5G 0.9736 0.5202 0.8495 1170 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.675 0.513 0.552 0.349\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 175/600 14.6G 0.9765 0.5195 0.8468 1563 640: 100% ━━━━━━━━━━━━ 270/270 4.3it/s 1:02<0.2ss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 9.9it/s 1.2s0.1s\n", " all 548 35895 0.675 0.513 0.551 0.349\n", "\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 100 epochs. Best results observed at epoch 75, best model saved as best.pt.\n", "To update EarlyStopping(patience=100) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n", "\n", "175 epochs completed in 3.111 hours.\n", "Optimizer stripped from /home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/last.pt, 52.0MB\n", "Optimizer stripped from /home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best.pt, 52.0MB\n", "\n", "Validating /home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best.pt...\n", "Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n", "Model summary (fused): 92 layers, 25,843,234 parameters, 0 gradients, 78.7 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 3.4it/s 3.5s0.1s\n", " all 548 35895 0.659 0.513 0.555 0.354\n", " person 531 13969 0.652 0.464 0.524 0.23\n", " van 421 1975 0.567 0.47 0.482 0.343\n", " car 515 14064 0.783 0.778 0.814 0.587\n", " motor 485 4886 0.606 0.408 0.463 0.209\n", " bus 131 251 0.776 0.554 0.632 0.476\n", " truck 266 750 0.569 0.403 0.413 0.278\n", "Speed: 0.0ms preprocess, 0.9ms inference, 0.0ms loss, 0.4ms postprocess per image\n", "Results saved to \u001b[1m/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final\u001b[0m\n" ] } ], "source": [ "from ultralytics import YOLO\n", "\n", "# Load a pretrained YOLO11n model\n", "model = YOLO('yolov8m.pt')\n", "\n", "train_results = model.train(\n", " data=\"/home/cuuva/experiment/vis6class_exp/vis6class.yaml\", #['person','car', 'truck', 'bus', 'motor','van]\n", " epochs=600,\n", " imgsz=640,\n", " batch=-1,\n", " device=\"cuda\",\n", " optimizer = 'AdamW',\n", " lr0 = 0.001,\n", " patience = 100,\n", " project = 'vis6class_v8m',\n", " name = '6class_final',\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "id": "681c71ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ultralytics 8.3.225 🚀 Python-3.10.18 torch-2.9.1+cu128 CUDA:0 (NVIDIA GeForce RTX 5090, 32109MiB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model summary (fused): 92 layers, 25,843,234 parameters, 0 gradients, 78.7 GFLOPs\n", "\n", "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 10, 8400) (49.6 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 ✅ 0.6s, saved as '/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.onnx' (98.8 MB)\n", "\n", "Export complete (0.7s)\n", "Results saved to \u001b[1m/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights\u001b[0m\n", "Predict: yolo predict task=detect model=/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.onnx imgsz=640 \n", "Validate: yolo val task=detect model=/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.onnx imgsz=640 data=/home/cuuva/experiment/vis6class_exp/vis6class.yaml \n", "Visualize: https://netron.app\n" ] }, { "data": { "text/plain": [ "'/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.onnx'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = YOLO(\"/home/cuuva/experiment/vis6class_exp/vis6class_v8m/6class_final/weights/best_vis_6class_final.pt\")\n", "model.export(format=\"onnx\", imgsz=640, device=0)" ] }, { "cell_type": "code", "execution_count": null, "id": "48b48641", "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 }