Created using Colaboratory

tutorial.ipynb

@@ -16,7 +16,7 @@
     "accelerator": "GPU",
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "1f8e9b8ebded4175b2eaa9f75c3ceb00": {
+        "b54ab52f1d4f4903897ab6cd49a3b9b2": {
           "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_0a1246a73077468ab80e979cc0576cd2",
+            "layout": "IPY_MODEL_1852f93fc2714d40adccb8aa161c42ff",
             "_model_module": "@jupyter-widgets/controls",
             "children": [
-              "IPY_MODEL_d327cde5a85a4a51bb8b1b3e9cf06c97",
-              "IPY_MODEL_d5ef1cb2cbed4b87b3c5d292ff2b0da6"
+              "IPY_MODEL_3293cfe869bd4a1bbbe18b49b6815de1",
+              "IPY_MODEL_8d5ee8b8ab6d46b98818bd2c562ddd1c"
             ]
           }
         },
-        "0a1246a73077468ab80e979cc0576cd2": {
+        "1852f93fc2714d40adccb8aa161c42ff": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -87,12 +87,12 @@
             "left": null
           }
         },
-        "d327cde5a85a4a51bb8b1b3e9cf06c97": {
+        "3293cfe869bd4a1bbbe18b49b6815de1": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "FloatProgressModel",
           "state": {
             "_view_name": "ProgressView",
-            "style": "IPY_MODEL_8d5dff8bca14435a88fa1814533acd85",
+            "style": "IPY_MODEL_49fcb2adb0354430b76f491af98abfe9",
             "_dom_classes": [],
             "description": "100%",
             "_model_name": "FloatProgressModel",
@@ -107,30 +107,30 @@
             "min": 0,
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_3d5136c19e7645ca9bc8f51ceffb2be1"
+            "layout": "IPY_MODEL_c7d76e0c53064363add56b8d05e561f5"
           }
         },
-        "d5ef1cb2cbed4b87b3c5d292ff2b0da6": {
+        "8d5ee8b8ab6d46b98818bd2c562ddd1c": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HTMLModel",
           "state": {
             "_view_name": "HTMLView",
-            "style": "IPY_MODEL_2919396dbd4b4c8e821d12bd28665d8a",
+            "style": "IPY_MODEL_48f321f789634aa584f8a29a3b925dd5",
             "_dom_classes": [],
             "description": "",
             "_model_name": "HTMLModel",
             "placeholder": "​",
             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": " 781M/781M [00:12<00:00, 65.5MB/s]",
+            "value": " 781M/781M [00:13<00:00, 62.6MB/s]",
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_6feb16f2b2fa4021b1a271e1dd442d04"
+            "layout": "IPY_MODEL_6610d6275f3e49d9937d50ed0a105947"
           }
         },
-        "8d5dff8bca14435a88fa1814533acd85": {
+        "49fcb2adb0354430b76f491af98abfe9": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ProgressStyleModel",
           "state": {
@@ -145,7 +145,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "3d5136c19e7645ca9bc8f51ceffb2be1": {
+        "c7d76e0c53064363add56b8d05e561f5": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -196,7 +196,7 @@
             "left": null
           }
         },
-        "2919396dbd4b4c8e821d12bd28665d8a": {
+        "48f321f789634aa584f8a29a3b925dd5": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "state": {
@@ -210,7 +210,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "6feb16f2b2fa4021b1a271e1dd442d04": {
+        "6610d6275f3e49d9937d50ed0a105947": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -261,7 +261,7 @@
             "left": null
           }
         },
-        "e6459e0bcee449b090fc9807672725bc": {
+        "0fffa335322b41658508e06aed0acbf0": {
           "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_c341e1d3bf3b40d1821ce392eb966c68",
+            "layout": "IPY_MODEL_a354c6f80ce347e5a3ef64af87c0eccb",
             "_model_module": "@jupyter-widgets/controls",
             "children": [
-              "IPY_MODEL_660afee173694231a6dce3cd94df6cae",
-              "IPY_MODEL_261218485cef48df961519dde5edfcbe"
+              "IPY_MODEL_85823e71fea54c39bd11e2e972348836",
+              "IPY_MODEL_fb11acd663fa4e71b041d67310d045fd"
             ]
           }
         },
-        "c341e1d3bf3b40d1821ce392eb966c68": {
+        "a354c6f80ce347e5a3ef64af87c0eccb": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -332,12 +332,12 @@
             "left": null
           }
         },
-        "660afee173694231a6dce3cd94df6cae": {
+        "85823e71fea54c39bd11e2e972348836": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "FloatProgressModel",
           "state": {
             "_view_name": "ProgressView",
-            "style": "IPY_MODEL_32736d503c06497abfae8c0421918255",
+            "style": "IPY_MODEL_8a919053b780449aae5523658ad611fa",
             "_dom_classes": [],
             "description": "100%",
             "_model_name": "FloatProgressModel",
@@ -352,30 +352,30 @@
             "min": 0,
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_e257738711f54d5280c8393d9d3dce1c"
+            "layout": "IPY_MODEL_5bae9393a58b44f7b69fb04816f94f6f"
           }
         },
-        "261218485cef48df961519dde5edfcbe": {
+        "fb11acd663fa4e71b041d67310d045fd": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HTMLModel",
           "state": {
             "_view_name": "HTMLView",
-            "style": "IPY_MODEL_beb7a6fe34b840899bb79c062681696f",
+            "style": "IPY_MODEL_d26c6d16c7f24030ab2da5285bf198ee",
             "_dom_classes": [],
             "description": "",
             "_model_name": "HTMLModel",
             "placeholder": "​",
             "_view_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "value": " 21.1M/21.1M [00:00<00:00, 33.5MB/s]",
+            "value": " 21.1M/21.1M [00:02<00:00, 9.36MB/s]",
             "_view_count": null,
             "_view_module_version": "1.5.0",
             "description_tooltip": null,
             "_model_module": "@jupyter-widgets/controls",
-            "layout": "IPY_MODEL_e639132395d64d70b99d8b72c32f8fbb"
+            "layout": "IPY_MODEL_f7767886b2364c8d9efdc79e175ad8eb"
           }
         },
-        "32736d503c06497abfae8c0421918255": {
+        "8a919053b780449aae5523658ad611fa": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ProgressStyleModel",
           "state": {
@@ -390,7 +390,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "e257738711f54d5280c8393d9d3dce1c": {
+        "5bae9393a58b44f7b69fb04816f94f6f": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -441,7 +441,7 @@
             "left": null
           }
         },
-        "beb7a6fe34b840899bb79c062681696f": {
+        "d26c6d16c7f24030ab2da5285bf198ee": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "state": {
@@ -455,7 +455,7 @@
             "_model_module": "@jupyter-widgets/controls"
           }
         },
-        "e639132395d64d70b99d8b72c32f8fbb": {
+        "f7767886b2364c8d9efdc79e175ad8eb": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "state": {
@@ -550,7 +550,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "ae8805a9-ce15-4e1c-f6b4-baa1c1033f56"
+        "outputId": "20027455-bf84-41fd-c902-b7282d53c91d"
       },
       "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": [
         {
           "output_type": "stream",
           "text": [
-            "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)\n"
+            "Setup complete. Using torch 1.8.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)\n"
           ],
           "name": "stdout"
         }
@@ -672,30 +672,30 @@
           "base_uri": "https://localhost:8080/",
           "height": 65,
           "referenced_widgets": [
-            "1f8e9b8ebded4175b2eaa9f75c3ceb00",
-            "0a1246a73077468ab80e979cc0576cd2",
-            "d327cde5a85a4a51bb8b1b3e9cf06c97",
-            "d5ef1cb2cbed4b87b3c5d292ff2b0da6",
-            "8d5dff8bca14435a88fa1814533acd85",
-            "3d5136c19e7645ca9bc8f51ceffb2be1",
-            "2919396dbd4b4c8e821d12bd28665d8a",
-            "6feb16f2b2fa4021b1a271e1dd442d04"
+            "b54ab52f1d4f4903897ab6cd49a3b9b2",
+            "1852f93fc2714d40adccb8aa161c42ff",
+            "3293cfe869bd4a1bbbe18b49b6815de1",
+            "8d5ee8b8ab6d46b98818bd2c562ddd1c",
+            "49fcb2adb0354430b76f491af98abfe9",
+            "c7d76e0c53064363add56b8d05e561f5",
+            "48f321f789634aa584f8a29a3b925dd5",
+            "6610d6275f3e49d9937d50ed0a105947"
           ]
         },
-        "outputId": "d6ace7c6-1be5-41ff-d607-1c716b88d298"
+        "outputId": "f0884441-78d9-443c-afa6-d00ec387908d"
       },
       "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",
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1f8e9b8ebded4175b2eaa9f75c3ceb00",
+              "model_id": "b54ab52f1d4f4903897ab6cd49a3b9b2",
               "version_minor": 0,
               "version_major": 2
             },
@@ -723,45 +723,45 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "cc25f70c-0a11-44f6-cc44-e92c5083488c"
+        "outputId": "5b54c11e-9f4b-4d9a-8e6e-6a2a4f0cc60d"
       },
       "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 v4.0-75-gbdd88e1 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 v4.0-133-g20d879d torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5x.pt to yolov5x.pt...\n",
-            "100% 168M/168M [00:04<00:00, 39.7MB/s]\n",
+            "100% 168M/168M [00:02<00:00, 59.1MB/s]\n",
             "\n",
             "Fusing layers... \n",
             "Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2824.78it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3236.68it/s]\n",
             "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n",
-            "               Class      Images     Targets           P           R      [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",
+            "               Class      Images      Labels           P           R      [email protected]  [email protected]:.95: 100% 157/157 [01:20<00:00,  1.95it/s]\n",
+            "                 all        5000       36335       0.749       0.619        0.68       0.486\n",
+            "Speed: 5.3/1.7/6.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
             "\n",
             "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
             "loading annotations into memory...\n",
-            "Done (t=0.44s)\n",
+            "Done (t=0.43s)\n",
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=4.47s)\n",
+            "DONE (t=5.10s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=94.87s).\n",
+            "DONE (t=88.52s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=15.96s).\n",
+            "DONE (t=17.17s).\n",
             " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501\n",
             " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.687\n",
             " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.544\n",
@@ -836,30 +836,30 @@
           "base_uri": "https://localhost:8080/",
           "height": 65,
           "referenced_widgets": [
-            "e6459e0bcee449b090fc9807672725bc",
-            "c341e1d3bf3b40d1821ce392eb966c68",
-            "660afee173694231a6dce3cd94df6cae",
-            "261218485cef48df961519dde5edfcbe",
-            "32736d503c06497abfae8c0421918255",
-            "e257738711f54d5280c8393d9d3dce1c",
-            "beb7a6fe34b840899bb79c062681696f",
-            "e639132395d64d70b99d8b72c32f8fbb"
+            "0fffa335322b41658508e06aed0acbf0",
+            "a354c6f80ce347e5a3ef64af87c0eccb",
+            "85823e71fea54c39bd11e2e972348836",
+            "fb11acd663fa4e71b041d67310d045fd",
+            "8a919053b780449aae5523658ad611fa",
+            "5bae9393a58b44f7b69fb04816f94f6f",
+            "d26c6d16c7f24030ab2da5285bf198ee",
+            "f7767886b2364c8d9efdc79e175ad8eb"
           ]
         },
-        "outputId": "e8b7d5b3-a71e-4446-eec2-ad13419cf700"
+        "outputId": "b41ac253-9e1b-4c26-d78b-700ea0154f43"
       },
       "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": "e6459e0bcee449b090fc9807672725bc",
+              "model_id": "0fffa335322b41658508e06aed0acbf0",
               "version_minor": 0,
               "version_major": 2
             },
@@ -924,27 +924,27 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "38e51b29-2df4-4f00-cde8-5f6e4a34da9e"
+        "outputId": "cf494627-09b9-4399-ff0c-fdb62b32340a"
       },
       "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 v4.0-75-gbdd88e1 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
+            "YOLOv5 v4.0-133-g20d879d torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
             "\n",
-            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
+            "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
             "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
             "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
-            "2021-02-12 06:38:28.027271: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
+            "2021-03-14 04:18:58.124672: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
             "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5s.pt to yolov5s.pt...\n",
-            "100% 14.1M/14.1M [00:01<00:00, 13.2MB/s]\n",
+            "100% 14.1M/14.1M [00:00<00:00, 63.1MB/s]\n",
             "\n",
             "\n",
             "                 from  n    params  module                                  arguments                     \n",
@@ -978,11 +978,11 @@
             "Transferred 362/362 items from yolov5s.pt\n",
             "Scaled weight_decay = 0.0005\n",
             "Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2566.00it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2956.76it/s]\n",
             "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../coco128/labels/train2017.cache\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 175.07it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 764773.38it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 128.17it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 205.30it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 604584.36it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 144.17it/s]\n",
             "Plotting labels... \n",
             "\n",
             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
@@ -991,21 +991,22 @@
             "Logging results to runs/train/exp\n",
             "Starting training for 3 epochs...\n",
             "\n",
-            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       0/2     3.27G   0.04357   0.06781   0.01869    0.1301       207       640: 100% 8/8 [00:03<00:00,  2.03it/s]\n",
-            "               Class      Images     Targets           P           R      [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",
+            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
+            "       0/2     3.29G   0.04237   0.06417   0.02121    0.1277       183       640: 100% 8/8 [00:03<00:00,  2.41it/s]\n",
+            "               Class      Images      Labels           P           R      [email protected]  [email protected]:.95: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
+            "                 all         128         929       0.642       0.637       0.661       0.432\n",
             "\n",
-            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       1/2     7.75G   0.04308   0.06654   0.02083    0.1304       227       640: 100% 8/8 [00:01<00:00,  4.11it/s]\n",
-            "               Class      Images     Targets           P           R      [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",
+            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
+            "       1/2     6.65G   0.04431   0.06403     0.019    0.1273       166       640: 100% 8/8 [00:01<00:00,  5.73it/s]\n",
+            "               Class      Images      Labels           P           R      [email protected]  [email protected]:.95: 100% 4/4 [00:01<00:00,  3.21it/s]\n",
+            "                 all         128         929       0.662       0.626       0.658       0.433\n",
             "\n",
-            "     Epoch   gpu_mem       box       obj       cls     total   targets  img_size\n",
-            "       2/2     7.75G   0.04461   0.06896   0.01866    0.1322       191       640: 100% 8/8 [00:02<00:00,  3.94it/s]\n",
-            "               Class      Images     Targets           P           R      [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",
+            "     Epoch   gpu_mem       box       obj       cls     total    labels  img_size\n",
+            "       2/2     6.65G   0.04506   0.06836   0.01913    0.1325       182       640: 100% 8/8 [00:01<00:00,  5.51it/s]\n",
+            "               Class      Images      Labels           P           R      [email protected]  [email protected]:.95: 100% 4/4 [00:02<00:00,  1.35it/s]\n",
+            "                 all         128         929       0.658       0.625       0.661       0.433\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
+            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
             "3 epochs completed in 0.007 hours.\n",
             "\n"
           ],
@@ -1247,4 +1248,4 @@
       "outputs": []
     }
   ]
-}
+}