Created using Colaboratory

tutorial.ipynb

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         "colab": {
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         },
-        "outputId": "c6ad57c2-40b7-4764-b07d-19ee2ceaabaf"
+        "outputId": "ae8805a9-ce15-4e1c-f6b4-baa1c1033f56"
       },
       "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'))"
       ],
-      "execution_count": null,
+      "execution_count": 1,
       "outputs": [
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           "text": [
-            "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)\n"
+            "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"
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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,
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       "outputs": [
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@@ -723,46 +723,45 @@
         "colab": {
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         },
-        "outputId": "427c211e-e283-4e87-f7b3-7b8dfb11a4a5"
+        "outputId": "cc25f70c-0a11-44f6-cc44-e92c5083488c"
       },
       "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": [
         {
           "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 v4.0-21-gb26a2f6 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130.5MB)\n",
+            "YOLOv5 v4.0-75-gbdd88e1 torch 1.7.0+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 [00:05<00:00, 31.9MB/s]\n",
+            "100% 168M/168M [00:04<00:00, 39.7MB/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/labels/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2791.81it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/labels/val2017.cache\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/labels/val2017.cache' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 13332180.55it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:30<00:00,  1.73it/s]\n",
-            "                 all       5e+03    3.63e+04       0.419       0.765        0.68       0.486\n",
-            "Speed: 5.2/2.0/7.2 ms inference/NMS/total per 640x640 image at batch-size 32\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[0mNew cache created: ../coco/val2017.cache\n",
+            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.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",
             "\n",
             "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
             "loading annotations into memory...\n",
-            "Done (t=0.41s)\n",
+            "Done (t=0.44s)\n",
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=5.26s)\n",
+            "DONE (t=4.47s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=93.97s).\n",
+            "DONE (t=94.87s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=15.06s).\n",
+            "DONE (t=15.96s).\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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+        "outputId": "e8b7d5b3-a71e-4446-eec2-ad13419cf700"
       },
       "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": 4,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8501ed1563e4452eac9df6b7a66e8f8c",
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         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "6af7116a-01ab-4b94-e5d7-b37c17dc95de"
+        "outputId": "38e51b29-2df4-4f00-cde8-5f6e4a34da9e"
       },
       "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": [
         {
           "output_type": "stream",
           "text": [
             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
-            "YOLOv5 v4.0-21-gb26a2f6 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130.5MB)\n",
+            "YOLOv5 v4.0-75-gbdd88e1 torch 1.7.0+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], 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='', 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",
-            "2021-01-17 19:56:03.945851: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\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",
             "\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 [00:00<00:00, 15.8MB/s]\n",
+            "100% 14.1M/14.1M [00:01<00:00, 13.2MB/s]\n",
             "\n",
             "\n",
             "                 from  n    params  module                                  arguments                     \n",
@@ -979,12 +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, 2647.74it/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, 2566.00it/s]\n",
             "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../coco128/labels/train2017.cache\n",
-            "\u001b[34m\u001b[1mtrain: \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, 1503840.09it/s]\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 176.03it/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, 24200.82it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 123.25it/s]\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",
             "Plotting labels... \n",
             "\n",
             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
@@ -994,19 +992,19 @@
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       0/2     3.27G   0.04357   0.06779   0.01869    0.1301       207       640: 100% 8/8 [00:04<00:00,  1.95it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:05<00:00,  1.36it/s]\n",
-            "                 all         128         929       0.392       0.732       0.657       0.428\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      [email protected]  [email protected]:.95: 100% 4/4 [00:04<00:00,  1.14s/it]\n",
+            "                 all         128         929       0.646       0.627       0.659       0.431\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       1/2     7.47G   0.04308   0.06636   0.02083    0.1303       227       640: 100% 8/8 [00:02<00:00,  3.88it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:01<00:00,  5.07it/s]\n",
-            "                 all         128         929       0.387       0.737       0.657       0.432\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      [email protected]  [email protected]:.95: 100% 4/4 [00:01<00:00,  2.94it/s]\n",
+            "                 all         128         929       0.681       0.607       0.663       0.434\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       2/2     7.48G   0.04461   0.06864   0.01866    0.1319       191       640: 100% 8/8 [00:02<00:00,  3.57it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:02<00:00,  2.82it/s]\n",
-            "                 all         128         929       0.385       0.742       0.658       0.431\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      [email protected]  [email protected]:.95: 100% 4/4 [00:03<00:00,  1.22it/s]\n",
+            "                 all         128         929       0.642       0.632       0.662       0.432\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
             "3 epochs completed in 0.007 hours.\n",
             "\n"
@@ -1238,4 +1236,4 @@
       "outputs": []
     }
   ]
-}
+}