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yolov5
提交 6f718cee authored 作者: Glenn Jocher's avatar Glenn Jocher
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tutorial.ipynb
浏览文件 @ 6f718cee
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"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
......@@ -563,12 +563,12 @@
"clear_output()\n",
"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
],
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"Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)\n"
"Setup complete. Using torch 1.8.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)\n"
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"source": [
"# Download COCO val2017\n",
"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
"!unzip -q tmp.zip -d ../ && rm tmp.zip"
],
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"source": [
"# Run YOLOv5x on COCO val2017\n",
"!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65"
],
"execution_count": null,
"execution_count": 3,
"outputs": [
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"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
"YOLOv5 v4.0-75-gbdd88e1 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"YOLOv5 v4.0-133-g20d879d torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5x.pt to yolov5x.pt...\n",
"100% 168M/168M [00:04<00:00, 39.7MB/s]\n",
"100% 168M/168M [00:02<00:00, 59.1MB/s]\n",
"\n",
"Fusing layers... \n",
"Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2824.78it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3236.68it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:33<00:00, 1.68it/s]\n",
" all 5e+03 3.63e+04 0.749 0.619 0.68 0.486\n",
"Speed: 5.2/2.0/7.3 ms inference/NMS/total per 640x640 image at batch-size 32\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:20<00:00, 1.95it/s]\n",
" all 5000 36335 0.749 0.619 0.68 0.486\n",
"Speed: 5.3/1.7/6.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
"\n",
"Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
"loading annotations into memory...\n",
"Done (t=0.44s)\n",
"Done (t=0.43s)\n",
"creating index...\n",
"index created!\n",
"Loading and preparing results...\n",
"DONE (t=4.47s)\n",
"DONE (t=5.10s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=94.87s).\n",
"DONE (t=88.52s).\n",
"Accumulating evaluation results...\n",
"DONE (t=15.96s).\n",
"DONE (t=17.17s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.687\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.544\n",
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"source": [
"# Download COCO128\n",
"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
"!unzip -q tmp.zip -d ../ && rm tmp.zip"
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"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
],
"execution_count": null,
"execution_count": 5,
"outputs": [
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"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 v4.0-75-gbdd88e1 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"YOLOv5 v4.0-133-g20d879d torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"\n",
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
"\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
"Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
"2021-02-12 06:38:28.027271: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-03-14 04:18:58.124672: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:01<00:00, 13.2MB/s]\n",
"100% 14.1M/14.1M [00:00<00:00, 63.1MB/s]\n",
"\n",
"\n",
" from n params module arguments \n",
......@@ -978,11 +978,11 @@
"Transferred 362/362 items from yolov5s.pt\n",
"Scaled weight_decay = 0.0005\n",
"Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2566.00it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2956.76it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 175.07it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 764773.38it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 128.17it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 205.30it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 604584.36it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 144.17it/s]\n",
"Plotting labels... \n",
"\n",
"\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
......@@ -991,21 +991,22 @@
"Logging results to runs/train/exp\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 0/2 3.27G 0.04357 0.06781 0.01869 0.1301 207 640: 100% 8/8 [00:03<00:00, 2.03it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00, 1.14s/it]\n",
" all 128 929 0.646 0.627 0.659 0.431\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 0/2 3.29G 0.04237 0.06417 0.02121 0.1277 183 640: 100% 8/8 [00:03<00:00, 2.41it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00, 1.04s/it]\n",
" all 128 929 0.642 0.637 0.661 0.432\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 1/2 7.75G 0.04308 0.06654 0.02083 0.1304 227 640: 100% 8/8 [00:01<00:00, 4.11it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.94it/s]\n",
" all 128 929 0.681 0.607 0.663 0.434\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 1/2 6.65G 0.04431 0.06403 0.019 0.1273 166 640: 100% 8/8 [00:01<00:00, 5.73it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 3.21it/s]\n",
" all 128 929 0.662 0.626 0.658 0.433\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 2/2 7.75G 0.04461 0.06896 0.01866 0.1322 191 640: 100% 8/8 [00:02<00:00, 3.94it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.22it/s]\n",
" all 128 929 0.642 0.632 0.662 0.432\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 2/2 6.65G 0.04506 0.06836 0.01913 0.1325 182 640: 100% 8/8 [00:01<00:00, 5.51it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.35it/s]\n",
" all 128 929 0.658 0.625 0.661 0.433\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
"3 epochs completed in 0.007 hours.\n",
"\n"
],
......@@ -1247,4 +1248,4 @@
"outputs": []
}
]
}
}
\ No newline at end of file
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