Created using Colaboratory

tutorial.ipynb

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@@ -517,8 +517,7 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
-        "<a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"        
+        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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     {
@@ -551,7 +550,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "9b022435-4197-41fc-abea-81f86ce857d0"
+        "outputId": "0cabe440-e06c-48b9-9180-4b4ea1790ff5"
       },
       "source": [
         "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
@@ -564,7 +563,7 @@
         "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": 1,
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         "id": "WQPtK1QYVaD_",
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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"
       ],
-      "execution_count": null,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "display_data",
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         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "2340b131-9943-4cd6-fd3a-8272aeb0774f"
+        "outputId": "a5d41761-f1a0-41fe-d0bb-4cceebd7c4a6"
       },
       "source": [
         "# Run YOLOv5x on COCO val2017\n",
-        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65"
+        "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
       ],
-      "execution_count": null,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "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 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, half=True, 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 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",
-            "100% 168M/168M [00:05<00:00, 32.3MB/s]\n",
+            "100% 168M/168M [00:01<00:00, 156MB/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' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3102.29it/s]\n",
+            "Model Summary: 476 layers, 87730285 parameters, 0 gradients\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3008.87it/s]\n",
             "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n",
-            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [01:23<00:00,  1.87it/s]\n",
-            "                 all        5000       36335       0.745       0.627        0.68        0.49\n",
-            "Speed: 5.3/1.6/6.9 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 [01:17<00:00,  2.02it/s]\n",
+            "                 all       5000      36335      0.746      0.626       0.68       0.49\n",
+            "Speed: 5.3/1.5/6.8 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=0.48s)\n",
+            "Done (t=0.44s)\n",
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=5.08s)\n",
+            "DONE (t=4.88s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=90.51s).\n",
+            "DONE (t=83.47s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=15.16s).\n",
+            "DONE (t=12.96s).\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",
@@ -827,32 +826,32 @@
         "id": "Knxi2ncxWffW",
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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"
       ],
-      "execution_count": null,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "0fffa335322b41658508e06aed0acbf0",
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@@ -918,23 +917,93 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "e715d09c-5d93-4912-a0df-9da0893f2014"
+        "outputId": "70004839-0c90-4bc0-c0e5-9a92f3e65b01"
       },
       "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"
+        "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
       ],
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
-            "YOLOv5 🚀 v5.0-2-g54d6516 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "\n",
+            "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=1, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)\n",
+            "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
+            "2021-06-08 16:52:25.719745: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] 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",
+            "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
+            "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt to yolov5s.pt...\n",
+            "100% 14.1M/14.1M [00:00<00:00, 18.7MB/s]\n",
+            "\n",
+            "\n",
+            "                 from  n    params  module                                  arguments                     \n",
+            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \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  1    156928  models.common.C3                        [128, 128, 3]                 \n",
+            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
+            "  6                -1  1    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",
+            " 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",
+            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
+            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
+            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
+            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
+            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
+            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
+            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
+            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
+            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
+            " 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",
+            "\n",
+            "Transferred 362/362 items from yolov5s.pt\n",
+            "\n",
+            "WARNING: Dataset not found, nonexistent paths: ['/content/coco128/images/train2017']\n",
+            "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip ...\n",
+            "100% 21.1M/21.1M [00:00<00:00, 68.2MB/s]\n",
+            "Dataset autodownload success\n",
+            "\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' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2036.51it/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, 189.76it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 687414.74it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 93.37it/s]\n",
+            "Plotting labels... \n",
+            "\n",
+            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
+            "Image sizes 640 train, 640 test\n",
+            "Using 2 dataloader workers\n",
+            "Logging results to runs/train/exp\n",
+            "Starting training for 1 epochs...\n",
+            "\n",
+            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
+            "       0/0     10.8G   0.04226   0.06068   0.02005     0.123       158       640: 100% 8/8 [00:05<00:00,  1.35it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:06<00:00,  1.53s/it]\n",
+            "                 all        128        929      0.633      0.641      0.668      0.439\n",
+            "1 epochs completed in 0.005 hours.\n",
+            "\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",
+            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
+            "YOLOv5 🚀 v5.0-157-gc6b51f4 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
-            "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, 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], label_smoothing=0.0, linear_lr=False, local_rank=-1, 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', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)\n",
+            "Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, 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], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)\n",
             "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
-            "2021-04-12 10:29:58.539457: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
+            "2021-06-08 16:53:03.275914: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] 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",
             "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
             "\n",
@@ -969,10 +1038,10 @@
             "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.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 796544.38it/s]\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 176.73it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 500812.42it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 134.10it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 824686.50it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 201.90it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 23766.92it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 98.35it/s]\n",
             "Plotting labels... \n",
             "\n",
             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
@@ -982,19 +1051,19 @@
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
-            "       0/2     3.29G   0.04368     0.065   0.02127    0.1299       183       640: 100% 8/8 [00:03<00:00,  2.21it/s]\n",
-            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:04<00:00,  1.09s/it]\n",
-            "                 all         128         929       0.605       0.657       0.666       0.434\n",
+            "       0/2     10.8G   0.04226   0.06067   0.02005     0.123       158       640: 100% 8/8 [00:05<00:00,  1.41it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00,  1.21s/it]\n",
+            "                 all        128        929      0.633      0.641      0.668      0.439\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
-            "       1/2     6.65G   0.04556    0.0651   0.01987    0.1305       166       640: 100% 8/8 [00:01<00:00,  5.18it/s]\n",
-            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:01<00:00,  2.72it/s]\n",
-            "                 all         128         929        0.61        0.66       0.669       0.438\n",
+            "       1/2     8.29G   0.04571   0.06616   0.01952    0.1314       164       640: 100% 8/8 [00:01<00:00,  5.65it/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      0.613      0.659      0.669      0.438\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
-            "       2/2     6.65G   0.04624   0.06923    0.0196    0.1351       182       640: 100% 8/8 [00:01<00:00,  5.19it/s]\n",
-            "               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:03<00:00,  1.27it/s]\n",
-            "                 all         128         929       0.618       0.659       0.671       0.438\n",
+            "       2/2     8.29G   0.04542    0.0718   0.01861    0.1358       191       640: 100% 8/8 [00:01<00:00,  4.89it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.48it/s]\n",
+            "                 all        128        929      0.636      0.652       0.67       0.44\n",
             "3 epochs completed in 0.007 hours.\n",
             "\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
@@ -1261,4 +1330,4 @@
       "outputs": []
     }
   ]
-}
+}