Created using Colaboratory

tutorial.ipynb

@@ -16,7 +16,7 @@
     "accelerator": "GPU",
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "02ac0588602847eea00a0205f87bcce2": {
+        "811fd52fef65422c8267bafcde8a2c3d": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HBoxModel",
           "state": {
@@ -28,15 +28,15 @@
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "box_style": "",
-            "layout": "IPY_MODEL_c472ea49806447a68b5a9221a4ddae85",
+            "layout": "IPY_MODEL_8f41b90117224eef9133a9c3a103dbba",
             "_model_module": "@jupyter-widgets/controls",
             "children": [
-              "IPY_MODEL_091fdf499bd44a80af7281d16da4aa93",
-              "IPY_MODEL_c79f69c959de4427ba102a87a9f46d80"
+              "IPY_MODEL_ca2fb37af6ed43d4a74cdc9f2ac5c4a5",
+              "IPY_MODEL_29419ae5ebb9403ea73f7e5a68037bdd"
             ]
           }
         },
-        "c472ea49806447a68b5a9221a4ddae85": {
+        "8f41b90117224eef9133a9c3a103dbba": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -87,12 +87,12 @@
             "left": null
           }
         },
-        "091fdf499bd44a80af7281d16da4aa93": {
+        "ca2fb37af6ed43d4a74cdc9f2ac5c4a5": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "FloatProgressModel",
           "state": {
             "_view_name": "ProgressView",
-            "style": "IPY_MODEL_c42ae5af74a0491187827d0a1fc259bb",
+            "style": "IPY_MODEL_6511b4dfb10b48d1bc98bcfb3987bfa0",
             "_dom_classes": [],
             "description": "100%",
             "_model_name": "FloatProgressModel",
@@ -107,30 +107,30 @@
             "min": 0,
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_5a90f72d3a2d46cb9ad915daa3ead8b4"
+            "layout": "IPY_MODEL_64f0badf1a8f489885aa984dd62d37dc"
           }
         },
-        "c79f69c959de4427ba102a87a9f46d80": {
+        "29419ae5ebb9403ea73f7e5a68037bdd": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HTMLModel",
           "state": {
             "_view_name": "HTMLView",
-            "style": "IPY_MODEL_2a7ed6611da34662b10e37fd4f4e4438",
+            "style": "IPY_MODEL_f569911c5cfc4d81bb1bdfa83447afc8",
             "_dom_classes": [],
             "description": "",
             "_model_name": "HTMLModel",
             "placeholder": "​",
             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": " 781M/781M [00:23<00:00, 35.1MB/s]",
+            "value": " 781M/781M [00:23<00:00, 34.2MB/s]",
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_fead0160658445bf9e966daa4481cad0"
+            "layout": "IPY_MODEL_84943ade566440aaa2dcf3b3b27e7074"
           }
         },
-        "c42ae5af74a0491187827d0a1fc259bb": {
+        "6511b4dfb10b48d1bc98bcfb3987bfa0": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ProgressStyleModel",
           "state": {
@@ -145,7 +145,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "5a90f72d3a2d46cb9ad915daa3ead8b4": {
+        "64f0badf1a8f489885aa984dd62d37dc": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -196,7 +196,7 @@
             "left": null
           }
         },
-        "2a7ed6611da34662b10e37fd4f4e4438": {
+        "f569911c5cfc4d81bb1bdfa83447afc8": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "state": {
@@ -210,7 +210,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "fead0160658445bf9e966daa4481cad0": {
+        "84943ade566440aaa2dcf3b3b27e7074": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -261,7 +261,7 @@
             "left": null
           }
         },
-        "cf1ab9fde7444d3e874fcd407ba8f0f8": {
+        "8501ed1563e4452eac9df6b7a66e8f8c": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HBoxModel",
           "state": {
@@ -273,15 +273,15 @@
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "box_style": "",
-            "layout": "IPY_MODEL_9ee03f9c85f34155b2645e89c9211547",
+            "layout": "IPY_MODEL_d2bb96801e1f46f4a58e02534f7026ff",
             "_model_module": "@jupyter-widgets/controls",
             "children": [
-              "IPY_MODEL_933ebc451c09490aadf71afbbb3dff2a",
-              "IPY_MODEL_8e7c55cbca624432a84fa7ad8f3a4016"
+              "IPY_MODEL_468a796ef06b4a24bcba6fbd4a0a8db5",
+              "IPY_MODEL_42ad5c1ea7be4835bffebf90642178f1"
             ]
           }
         },
-        "9ee03f9c85f34155b2645e89c9211547": {
+        "d2bb96801e1f46f4a58e02534f7026ff": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -332,50 +332,50 @@
             "left": null
           }
         },
-        "933ebc451c09490aadf71afbbb3dff2a": {
+        "468a796ef06b4a24bcba6fbd4a0a8db5": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "FloatProgressModel",
           "state": {
             "_view_name": "ProgressView",
-            "style": "IPY_MODEL_dd62d83b35d04a178840772e82bd2f2e",
+            "style": "IPY_MODEL_c58b5536d98f4814831934e9c30c4d78",
             "_dom_classes": [],
             "description": "100%",
             "_model_name": "FloatProgressModel",
             "bar_style": "success",
-            "max": 22090455,
+            "max": 22091032,
             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": 22090455,
+            "value": 22091032,
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "orientation": "horizontal",
             "min": 0,
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_d5c4f3d1c8b046e3a163faaa6b3a51ab"
+            "layout": "IPY_MODEL_505597101151486ea29e9ab754544d27"
           }
         },
-        "8e7c55cbca624432a84fa7ad8f3a4016": {
+        "42ad5c1ea7be4835bffebf90642178f1": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HTMLModel",
           "state": {
             "_view_name": "HTMLView",
-            "style": "IPY_MODEL_78d1da8efb504b03878ca9ce5b404006",
+            "style": "IPY_MODEL_de6e7b4b4a1c408c9f89d89b07a13bcd",
             "_dom_classes": [],
             "description": "",
             "_model_name": "HTMLModel",
             "placeholder": "​",
             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": " 21.1M/21.1M [00:01<00:00, 16.9MB/s]",
+            "value": " 21.1M/21.1M [00:01<00:00, 18.2MB/s]",
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_d28208ba1213436a93926a01d99d97ae"
+            "layout": "IPY_MODEL_f5cc9c7d4c274b2d81327ba3163c43fd"
           }
         },
-        "dd62d83b35d04a178840772e82bd2f2e": {
+        "c58b5536d98f4814831934e9c30c4d78": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ProgressStyleModel",
           "state": {
@@ -390,7 +390,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "d5c4f3d1c8b046e3a163faaa6b3a51ab": {
+        "505597101151486ea29e9ab754544d27": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -441,7 +441,7 @@
             "left": null
           }
         },
-        "78d1da8efb504b03878ca9ce5b404006": {
+        "de6e7b4b4a1c408c9f89d89b07a13bcd": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "state": {
@@ -455,7 +455,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "d28208ba1213436a93926a01d99d97ae": {
+        "f5cc9c7d4c274b2d81327ba3163c43fd": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -550,7 +550,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "888d5c41-00e9-47d8-d230-dded99325bea"
+        "outputId": "c6ad57c2-40b7-4764-b07d-19ee2ceaabaf"
       },
       "source": [
         "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
@@ -563,7 +563,7 @@
         "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": 3,
       "outputs": [
         {
           "output_type": "stream",
@@ -670,32 +670,32 @@
         "id": "WQPtK1QYVaD_",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 66,
+          "height": 65,
           "referenced_widgets": [
-            "02ac0588602847eea00a0205f87bcce2",
-            "c472ea49806447a68b5a9221a4ddae85",
-            "091fdf499bd44a80af7281d16da4aa93",
-            "c79f69c959de4427ba102a87a9f46d80",
-            "c42ae5af74a0491187827d0a1fc259bb",
-            "5a90f72d3a2d46cb9ad915daa3ead8b4",
-            "2a7ed6611da34662b10e37fd4f4e4438",
-            "fead0160658445bf9e966daa4481cad0"
+            "811fd52fef65422c8267bafcde8a2c3d",
+            "8f41b90117224eef9133a9c3a103dbba",
+            "ca2fb37af6ed43d4a74cdc9f2ac5c4a5",
+            "29419ae5ebb9403ea73f7e5a68037bdd",
+            "6511b4dfb10b48d1bc98bcfb3987bfa0",
+            "64f0badf1a8f489885aa984dd62d37dc",
+            "f569911c5cfc4d81bb1bdfa83447afc8",
+            "84943ade566440aaa2dcf3b3b27e7074"
           ]
         },
-        "outputId": "780d8f5f-766e-4b99-e370-11f9b884c27a"
+        "outputId": "59a7a546-8492-492e-861d-70a2c85a6794"
       },
       "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": 6,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "02ac0588602847eea00a0205f87bcce2",
+              "model_id": "811fd52fef65422c8267bafcde8a2c3d",
               "version_minor": 0,
               "version_major": 2
             },
@@ -723,56 +723,58 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79"
+        "outputId": "427c211e-e283-4e87-f7b3-7b8dfb11a4a5"
       },
       "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": 7,
       "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_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
-            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
+            "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",
             "\n",
-            "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
-            "100% 170M/170M [00:05<00:00, 32.6MB/s]\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",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 484 layers, 88922205 parameters, 0 gradients\n",
-            "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:30<00:00,  1.74it/s]\n",
-            "                 all       5e+03    3.63e+04       0.409       0.754       0.672       0.484\n",
-            "Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\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",
             "\n",
             "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
             "loading annotations into memory...\n",
-            "Done (t=0.43s)\n",
+            "Done (t=0.41s)\n",
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=4.67s)\n",
+            "DONE (t=5.26s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=92.11s).\n",
+            "DONE (t=93.97s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=13.24s).\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.492\n",
-            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.676\n",
-            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.534\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.376\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.617\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.670\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
+            "DONE (t=15.06s).\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",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.338\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.548\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.637\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.378\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.628\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.680\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.520\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.729\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826\n",
             "Results saved to runs/test/exp\n"
           ],
           "name": "stdout"
@@ -833,37 +835,37 @@
         "id": "Knxi2ncxWffW",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 66,
+          "height": 65,
           "referenced_widgets": [
-            "cf1ab9fde7444d3e874fcd407ba8f0f8",
-            "9ee03f9c85f34155b2645e89c9211547",
-            "933ebc451c09490aadf71afbbb3dff2a",
-            "8e7c55cbca624432a84fa7ad8f3a4016",
-            "dd62d83b35d04a178840772e82bd2f2e",
-            "d5c4f3d1c8b046e3a163faaa6b3a51ab",
-            "78d1da8efb504b03878ca9ce5b404006",
-            "d28208ba1213436a93926a01d99d97ae"
+            "8501ed1563e4452eac9df6b7a66e8f8c",
+            "d2bb96801e1f46f4a58e02534f7026ff",
+            "468a796ef06b4a24bcba6fbd4a0a8db5",
+            "42ad5c1ea7be4835bffebf90642178f1",
+            "c58b5536d98f4814831934e9c30c4d78",
+            "505597101151486ea29e9ab754544d27",
+            "de6e7b4b4a1c408c9f89d89b07a13bcd",
+            "f5cc9c7d4c274b2d81327ba3163c43fd"
           ]
         },
-        "outputId": "59f9a94b-21e1-4626-f36a-a8e1b1e5c8f6"
+        "outputId": "c68a3db4-1314-46b4-9e52-83532eb65749"
       },
       "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": "cf1ab9fde7444d3e874fcd407ba8f0f8",
+              "model_id": "8501ed1563e4452eac9df6b7a66e8f8c",
               "version_minor": 0,
               "version_major": 2
             },
             "text/plain": [
-              "HBox(children=(FloatProgress(value=0.0, max=22090455.0), HTML(value='')))"
+              "HBox(children=(FloatProgress(value=0.0, max=22091032.0), HTML(value='')))"
             ]
           },
           "metadata": {
@@ -923,86 +925,90 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "138f2d1d-364c-405a-cf13-ea91a2aff915"
+        "outputId": "6af7116a-01ab-4b94-e5d7-b37c17dc95de"
       },
       "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": [
-            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
+            "\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",
             "\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_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', 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], 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",
-            "2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
-            "Hyperparameters {'lr0': 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/v3.1/yolov5s.pt to yolov5s.pt...\n",
-            "100% 14.5M/14.5M [00:01<00:00, 14.8MB/s]\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",
+            "\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",
             "\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     19904  models.common.BottleneckCSP             [64, 64, 1]                   \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    161152  models.common.BottleneckCSP             [128, 128, 3]                 \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    641792  models.common.BottleneckCSP             [256, 256, 3]                 \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   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          \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    378624  models.common.BottleneckCSP             [512, 256, 1, False]          \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     95104  models.common.BottleneckCSP             [256, 128, 1, False]          \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    313088  models.common.BottleneckCSP             [256, 256, 1, False]          \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   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          \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, 7468157 parameters, 7468157 gradients\n",
+            "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n",
             "\n",
-            "Transferred 370/370 items from yolov5s.pt\n",
-            "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
-            "Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n",
-            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n",
-            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n",
-            "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n",
-            "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n",
-            "NumExpr defaulting to 2 threads.\n",
+            "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[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",
+            "Plotting labels... \n",
             "\n",
-            "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\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 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       0/2     5.24G   0.04202   0.06745   0.01503    0.1245       194       640: 100% 8/8 [00:03<00:00,  2.01it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:03<00:00,  2.40it/s]\n",
-            "                 all         128         929       0.404       0.758       0.701        0.45\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",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       1/2     5.12G   0.04461   0.05874    0.0169    0.1202       142       640: 100% 8/8 [00:01<00:00,  4.14it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:01<00:00,  5.75it/s]\n",
-            "                 all         128         929       0.403       0.772       0.703       0.453\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",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       2/2     5.12G   0.04445   0.06545   0.01667    0.1266       149       640: 100% 8/8 [00:01<00:00,  4.15it/s]\n",
-            "               Class      Images     Targets           P           R      [email protected]  [email protected]:.95: 100% 8/8 [00:06<00:00,  1.18it/s]\n",
-            "                 all         128         929       0.395       0.767       0.702       0.452\n",
-            "Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n",
-            "3 epochs completed in 0.006 hours.\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",
+            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
+            "3 epochs completed in 0.007 hours.\n",
             "\n"
           ],
           "name": "stdout"