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tutorial.ipynb
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},
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"outputId": "e2e839d5-d6fc-409c-e44c-0b0b6aa9319d"
},
"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
......@@ -415,14 +415,14 @@
"clear_output()\n",
"print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
],
"execution_count": null,
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setup complete. Using torch 1.9.0+cu102 (Tesla V100-SXM2-16GB)\n"
],
"name": "stdout"
"Setup complete. Using torch 1.10.0+cu102 (Tesla V100-SXM2-16GB)\n"
]
}
]
},
......@@ -454,28 +454,28 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8b728908-81ab-4861-edb0-4d0c46c439fb"
"outputId": "8f7e6588-215d-4ebd-93af-88b871e770a7"
},
"source": [
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
"Image(filename='runs/detect/exp/zidane.jpg', width=600)"
],
"execution_count": null,
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images/, imgsz=640, conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False\n",
"YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
"YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\n",
"Fusing layers... \n",
"Model Summary: 224 layers, 7266973 parameters, 0 gradients\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.007s)\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.007s)\n",
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n",
"Done. (0.091s)\n"
],
"name": "stdout"
"Model Summary: 213 layers, 7225885 parameters, 0 gradients\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.007s)\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.007s)\n",
"Speed: 0.5ms pre-process, 6.9ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
]
}
]
},
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"source": [
"# Download COCO val\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
],
"execution_count": null,
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
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"model_id": "484511f272e64eab8b42e68dac5f7a66",
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"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3dd0e2fc-aecf-4108-91b1-6392da1863cb"
"outputId": "74f1dfa9-6b6d-4b36-f67e-bbae243869f9"
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"source": [
"# Run YOLOv5x on COCO val\n",
"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
],
"execution_count": null,
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mdata=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True\n",
"YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True\n",
"YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
"100% 168M/168M [00:08<00:00, 20.6MB/s]\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5x.pt to yolov5x.pt...\n",
"100% 166M/166M [00:03<00:00, 54.1MB/s]\n",
"\n",
"Fusing layers... \n",
"Model Summary: 476 layers, 87730285 parameters, 0 gradients\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2749.96it/s]\n",
"Model Summary: 444 layers, 86705005 parameters, 0 gradients\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2636.64it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../datasets/coco/val2017.cache\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:08<00:00, 2.28it/s]\n",
" all 5000 36335 0.746 0.626 0.68 0.49\n",
"Speed: 0.1ms pre-process, 5.1ms inference, 1.6ms NMS per image at shape (32, 3, 640, 640)\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:12<00:00, 2.17it/s]\n",
" all 5000 36335 0.729 0.63 0.683 0.496\n",
"Speed: 0.1ms pre-process, 4.9ms inference, 1.9ms NMS per image at shape (32, 3, 640, 640)\n",
"\n",
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
"loading annotations into memory...\n",
......@@ -595,29 +594,28 @@
"creating index...\n",
"index created!\n",
"Loading and preparing results...\n",
"DONE (t=4.94s)\n",
"DONE (t=5.15s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=83.60s).\n",
"DONE (t=90.39s).\n",
"Accumulating evaluation results...\n",
"DONE (t=13.22s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.504\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.546\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.629\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.681\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n",
"DONE (t=14.54s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.552\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.381\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.630\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.732\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829\n",
"Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
],
"name": "stdout"
]
}
]
},
......@@ -722,37 +720,37 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "00ea4b14-a75c-44a2-a913-03b431b69de5"
"outputId": "8724d13d-6711-4a12-d96a-1c655e5c3549"
},
"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
],
"execution_count": null,
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v5.0-367-g01cdb76 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
"YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\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, copy_paste=0.0\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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, copy_paste=0.0\n",
"\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
"2021-08-15 14:40:43.449642: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 3 156928 models.common.C3 [128, 128, 3] \n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n",
" 9 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
......@@ -768,48 +766,121 @@
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
"Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",
"Model Summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.5 GFLOPs\n",
"\n",
"Transferred 362/362 items from yolov5s.pt\n",
"Transferred 349/349 items from yolov5s.pt\n",
"Scaled weight_decay = 0.0005\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 57 weight, 60 weight (no decay), 60 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2440.28it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../datasets/coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 302.61it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 296.04it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 142.55it/s]\n",
"[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
"[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 121.58it/s]\n",
"Plotting labels... \n",
"\n",
"\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.27, Best Possible Recall (BPR) = 0.9935\n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to runs/train/exp\n",
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch gpu_mem box obj cls labels img_size\n",
" 0/2 3.64G 0.04492 0.0674 0.02213 298 640: 100% 8/8 [00:03<00:00, 2.05it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.70it/s]\n",
" all 128 929 0.686 0.565 0.642 0.421\n",
" 0/2 3.62G 0.04621 0.0711 0.02112 203 640: 100% 8/8 [00:04<00:00, 1.99it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.37it/s]\n",
" all 128 929 0.655 0.547 0.622 0.41\n",
"\n",
" Epoch gpu_mem box obj cls labels img_size\n",
" 1/2 5.04G 0.04403 0.0611 0.01986 232 640: 100% 8/8 [00:01<00:00, 5.59it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.46it/s]\n",
" all 128 929 0.694 0.563 0.654 0.425\n",
" 1/2 5.31G 0.04564 0.06898 0.02116 143 640: 100% 8/8 [00:01<00:00, 4.77it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.27it/s]\n",
" all 128 929 0.68 0.554 0.632 0.419\n",
"\n",
" Epoch gpu_mem box obj cls labels img_size\n",
" 2/2 5.04G 0.04616 0.07056 0.02071 214 640: 100% 8/8 [00:01<00:00, 5.94it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.52it/s]\n",
" all 128 929 0.711 0.562 0.66 0.431\n",
" 2/2 5.31G 0.04487 0.06883 0.01998 253 640: 100% 8/8 [00:01<00:00, 4.91it/s]\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.30it/s]\n",
" all 128 929 0.71 0.544 0.629 0.423\n",
"\n",
"3 epochs completed in 0.003 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB\n",
"\n",
"3 epochs completed in 0.005 hours.\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",
"Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model Summary: 213 layers, 7225885 parameters, 0 gradients, 16.5 GFLOPs\n",
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.04it/s]\n",
" all 128 929 0.71 0.544 0.63 0.423\n",
" person 128 254 0.816 0.669 0.774 0.507\n",
" bicycle 128 6 0.799 0.667 0.614 0.371\n",
" car 128 46 0.803 0.355 0.486 0.209\n",
" motorcycle 128 5 0.704 0.6 0.791 0.583\n",
" airplane 128 6 1 0.795 0.995 0.717\n",
" bus 128 7 0.656 0.714 0.72 0.606\n",
" train 128 3 0.852 1 0.995 0.682\n",
" truck 128 12 0.521 0.25 0.395 0.215\n",
" boat 128 6 0.795 0.333 0.445 0.137\n",
" traffic light 128 14 0.576 0.143 0.24 0.161\n",
" stop sign 128 2 0.636 0.5 0.828 0.713\n",
" bench 128 9 0.972 0.444 0.575 0.25\n",
" bird 128 16 0.939 0.968 0.988 0.645\n",
" cat 128 4 0.984 0.75 0.822 0.694\n",
" dog 128 9 0.888 0.667 0.903 0.54\n",
" horse 128 2 0.689 1 0.995 0.697\n",
" elephant 128 17 0.96 0.882 0.943 0.681\n",
" bear 128 1 0.549 1 0.995 0.995\n",
" zebra 128 4 0.86 1 0.995 0.952\n",
" giraffe 128 9 0.822 0.778 0.905 0.57\n",
" backpack 128 6 1 0.309 0.457 0.195\n",
" umbrella 128 18 0.775 0.576 0.74 0.418\n",
" handbag 128 19 0.628 0.105 0.167 0.111\n",
" tie 128 7 0.96 0.571 0.701 0.441\n",
" suitcase 128 4 1 0.895 0.995 0.621\n",
" frisbee 128 5 0.641 0.8 0.798 0.664\n",
" skis 128 1 0.627 1 0.995 0.497\n",
" snowboard 128 7 0.988 0.714 0.768 0.556\n",
" sports ball 128 6 0.671 0.5 0.579 0.339\n",
" kite 128 10 0.631 0.515 0.598 0.221\n",
" baseball bat 128 4 0.47 0.456 0.277 0.137\n",
" baseball glove 128 7 0.459 0.429 0.334 0.182\n",
" skateboard 128 5 0.7 0.48 0.736 0.548\n",
" tennis racket 128 7 0.559 0.571 0.538 0.315\n",
" bottle 128 18 0.607 0.389 0.484 0.282\n",
" wine glass 128 16 0.722 0.812 0.82 0.385\n",
" cup 128 36 0.881 0.361 0.532 0.312\n",
" fork 128 6 0.384 0.167 0.239 0.191\n",
" knife 128 16 0.908 0.616 0.681 0.443\n",
" spoon 128 22 0.836 0.364 0.536 0.264\n",
" bowl 128 28 0.793 0.536 0.633 0.471\n",
" banana 128 1 0 0 0.142 0.0995\n",
" sandwich 128 2 0 0 0.0951 0.0717\n",
" orange 128 4 1 0 0.67 0.317\n",
" broccoli 128 11 0.345 0.182 0.283 0.243\n",
" carrot 128 24 0.688 0.459 0.612 0.402\n",
" hot dog 128 2 0.424 0.771 0.497 0.473\n",
" pizza 128 5 0.622 1 0.824 0.551\n",
" donut 128 14 0.703 1 0.952 0.853\n",
" cake 128 4 0.733 1 0.945 0.777\n",
" chair 128 35 0.512 0.486 0.488 0.222\n",
" couch 128 6 0.68 0.36 0.746 0.406\n",
" potted plant 128 14 0.797 0.714 0.808 0.482\n",
" bed 128 3 1 0 0.474 0.318\n",
" dining table 128 13 0.852 0.445 0.478 0.315\n",
" toilet 128 2 0.512 0.5 0.554 0.487\n",
" tv 128 2 0.754 1 0.995 0.895\n",
" laptop 128 3 1 0 0.39 0.147\n",
" mouse 128 2 1 0 0.0283 0.0226\n",
" remote 128 8 0.747 0.625 0.636 0.488\n",
" cell phone 128 8 0.555 0.166 0.417 0.222\n",
" microwave 128 3 0.417 1 0.995 0.732\n",
" oven 128 5 0.37 0.4 0.432 0.249\n",
" sink 128 6 0.356 0.167 0.269 0.149\n",
" refrigerator 128 5 0.705 0.8 0.814 0.45\n",
" book 128 29 0.628 0.138 0.298 0.136\n",
" clock 128 9 0.857 0.778 0.893 0.574\n",
" vase 128 2 0.242 1 0.663 0.622\n",
" scissors 128 1 1 0 0.0207 0.00207\n",
" teddy bear 128 21 0.847 0.381 0.622 0.345\n",
" toothbrush 128 5 0.99 0.6 0.662 0.45\n",
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
],
"name": "stdout"
]
}
]
},
......@@ -953,19 +1024,19 @@
"%%shell\n",
"export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n",
"rm -rf runs # remove runs/\n",
"for m in yolov5s; do # models\n",
" python train.py --weights $m.pt --epochs 3 --img 320 --device 0 # train pretrained\n",
" python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0 # train scratch\n",
"for m in yolov5n; do # models\n",
" python train.py --img 64 --batch 32 --weights $m.pt --epochs 1 --device 0 # train pretrained\n",
" python train.py --img 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device 0 # train scratch\n",
" for d in 0 cpu; do # devices\n",
" python detect.py --weights $m.pt --device $d # detect official\n",
" python detect.py --weights runs/train/exp/weights/best.pt --device $d # detect custom\n",
" python val.py --weights $m.pt --device $d # val official\n",
" python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",
" python detect.py --weights $m.pt --device $d # detect official\n",
" python detect.py --weights runs/train/exp/weights/best.pt --device $d # detect custom\n",
" done\n",
"python hubconf.py # hub\n",
"python models/yolo.py --cfg $m.yaml # build PyTorch model\n",
"python models/tf.py --weights $m.pt # build TensorFlow model\n",
"python export.py --img 128 --batch 1 --weights $m.pt --include torchscript onnx # export\n",
" python hubconf.py # hub\n",
" python models/yolo.py --cfg $m.yaml # build PyTorch model\n",
" python models/tf.py --weights $m.pt # build TensorFlow model\n",
" python export.py --img 64 --batch 1 --weights $m.pt --include torchscript onnx # export\n",
"done"
],
"execution_count": null,
......
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