From 78cf4885565302603fd1b211d498160bdf88ad38 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 8 Jun 2021 18:54:33 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 259 +++++++++++++++++++++++++++++++------------------ 1 file changed, 164 insertions(+), 95 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 4e760b13b..4429c1044 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -16,7 +16,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "8815626359d84416a2f44a95500580a4": { + "cef5e9351ca743bcba5febac0b096a30": { "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_3b85609c4ce94a74823f2cfe141ce68e", + "layout": "IPY_MODEL_ec326c52378f4410920c328f221e0514", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_876609753c2946248890344722963d44", - "IPY_MODEL_8abfdd8778e44b7ca0d29881cb1ada05" + "IPY_MODEL_83000c64a11c4ae8abd6f0ef2f108cef", + "IPY_MODEL_0f7899eb719f4a9c9852426551f97be9" ] } }, - "3b85609c4ce94a74823f2cfe141ce68e": { + "ec326c52378f4410920c328f221e0514": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -87,12 +87,12 @@ "left": null } }, - "876609753c2946248890344722963d44": { + "83000c64a11c4ae8abd6f0ef2f108cef": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_78c6c3d97c484916b8ee167c63556800", + "style": "IPY_MODEL_886ac5b18b3c4c82bf15ad5055f1e17e", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -107,30 +107,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_9dd0f182db5d45378ceafb855e486eb8" + "layout": "IPY_MODEL_4e67b3c3a49849c7a7ba28b7eec96e7a" } }, - "8abfdd8778e44b7ca0d29881cb1ada05": { + "0f7899eb719f4a9c9852426551f97be9": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_a3dab28b45c247089a3d1b8b09f327de", + "style": "IPY_MODEL_62c3682ff1804571a483d46664533969", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": " 781M/781M [08:43<00:00, 1.56MB/s]", + "value": " 781M/781M [00:12<00:00, 67.1MB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_32451332b7a94ba9aacddeaa6ac94d50" + "layout": "IPY_MODEL_599dda3b608b432393760b2ca4ae7c7d" } }, - "78c6c3d97c484916b8ee167c63556800": { + "886ac5b18b3c4c82bf15ad5055f1e17e": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -145,7 +145,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "9dd0f182db5d45378ceafb855e486eb8": { + "4e67b3c3a49849c7a7ba28b7eec96e7a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -196,7 +196,7 @@ "left": null } }, - "a3dab28b45c247089a3d1b8b09f327de": { + "62c3682ff1804571a483d46664533969": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -210,7 +210,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "32451332b7a94ba9aacddeaa6ac94d50": { + "599dda3b608b432393760b2ca4ae7c7d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -261,7 +261,7 @@ "left": null } }, - "0fffa335322b41658508e06aed0acbf0": { + "217ca488c82a4b7a80318b70887a556e": { "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_a354c6f80ce347e5a3ef64af87c0eccb", + "layout": "IPY_MODEL_4e63af16f1084ca98a6fa5a282f2a81e", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_85823e71fea54c39bd11e2e972348836", - "IPY_MODEL_fb11acd663fa4e71b041d67310d045fd" + "IPY_MODEL_49f4b3c7f6ff42b4b9132a8550e12186", + "IPY_MODEL_8ec9e1a4883245daaf029458ee09721f" ] } }, - "a354c6f80ce347e5a3ef64af87c0eccb": { + "4e63af16f1084ca98a6fa5a282f2a81e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -332,12 +332,12 @@ "left": null } }, - "85823e71fea54c39bd11e2e972348836": { + "49f4b3c7f6ff42b4b9132a8550e12186": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_8a919053b780449aae5523658ad611fa", + "style": "IPY_MODEL_9d3e775ee11e4cf4b587b64fbc3cc6f7", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -352,30 +352,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_5bae9393a58b44f7b69fb04816f94f6f" + "layout": "IPY_MODEL_70f68a9a51ac46e6ab7e51fb4fc6bda3" } }, - "fb11acd663fa4e71b041d67310d045fd": { + "8ec9e1a4883245daaf029458ee09721f": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_d26c6d16c7f24030ab2da5285bf198ee", + "style": "IPY_MODEL_fdb8ab377c114bc3b862ba76eb93cef7", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "value": " 21.1M/21.1M [00:02<00:00, 9.36MB/s]", + "value": " 21.1M/21.1M [00:36<00:00, 605kB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_f7767886b2364c8d9efdc79e175ad8eb" + "layout": "IPY_MODEL_cd267c153c244621a1f50706d2ddc897" } }, - "8a919053b780449aae5523658ad611fa": { + "9d3e775ee11e4cf4b587b64fbc3cc6f7": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -390,7 +390,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "5bae9393a58b44f7b69fb04816f94f6f": { + "70f68a9a51ac46e6ab7e51fb4fc6bda3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -441,7 +441,7 @@ "left": null } }, - "d26c6d16c7f24030ab2da5285bf198ee": { + "fdb8ab377c114bc3b862ba76eb93cef7": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -455,7 +455,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "f7767886b2364c8d9efdc79e175ad8eb": { + "cd267c153c244621a1f50706d2ddc897": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -517,8 +517,7 @@ "colab_type": "text" }, "source": [ - "\"Open", - "\"Kaggle\"" + "\"Open" ] }, { @@ -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, "outputs": [ { "output_type": "stream", @@ -663,32 +662,32 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 65, + "height": 66, "referenced_widgets": [ - "8815626359d84416a2f44a95500580a4", - "3b85609c4ce94a74823f2cfe141ce68e", - "876609753c2946248890344722963d44", - "8abfdd8778e44b7ca0d29881cb1ada05", - "78c6c3d97c484916b8ee167c63556800", - "9dd0f182db5d45378ceafb855e486eb8", - "a3dab28b45c247089a3d1b8b09f327de", - "32451332b7a94ba9aacddeaa6ac94d50" + "cef5e9351ca743bcba5febac0b096a30", + "ec326c52378f4410920c328f221e0514", + "83000c64a11c4ae8abd6f0ef2f108cef", + "0f7899eb719f4a9c9852426551f97be9", + "886ac5b18b3c4c82bf15ad5055f1e17e", + "4e67b3c3a49849c7a7ba28b7eec96e7a", + "62c3682ff1804571a483d46664533969", + "599dda3b608b432393760b2ca4ae7c7d" ] }, - "outputId": "81521192-cf67-4a47-a4cc-434cb0ebc363" + "outputId": "56b6402a-81d5-41d0-a3c8-8889db1fca6c" }, "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": "8815626359d84416a2f44a95500580a4", + "model_id": "cef5e9351ca743bcba5febac0b096a30", "version_minor": 0, "version_major": 2 }, @@ -716,45 +715,45 @@ "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", "colab": { "base_uri": "https://localhost:8080/", - "height": 65, + "height": 66, "referenced_widgets": [ - "0fffa335322b41658508e06aed0acbf0", - "a354c6f80ce347e5a3ef64af87c0eccb", - "85823e71fea54c39bd11e2e972348836", - "fb11acd663fa4e71b041d67310d045fd", - "8a919053b780449aae5523658ad611fa", - "5bae9393a58b44f7b69fb04816f94f6f", - "d26c6d16c7f24030ab2da5285bf198ee", - "f7767886b2364c8d9efdc79e175ad8eb" + "217ca488c82a4b7a80318b70887a556e", + "4e63af16f1084ca98a6fa5a282f2a81e", + "49f4b3c7f6ff42b4b9132a8550e12186", + "8ec9e1a4883245daaf029458ee09721f", + "9d3e775ee11e4cf4b587b64fbc3cc6f7", + "70f68a9a51ac46e6ab7e51fb4fc6bda3", + "fdb8ab377c114bc3b862ba76eb93cef7", + "cd267c153c244621a1f50706d2ddc897" ] }, - "outputId": "b41ac253-9e1b-4c26-d78b-700ea0154f43" + "outputId": "9e4788c2-e1d4-4a13-c3d2-984f5df7ffab" }, "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", + "model_id": "217ca488c82a4b7a80318b70887a556e", "version_minor": 0, "version_major": 2 }, @@ -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=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=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", - "2021-04-12 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 16:52:25.719745: 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=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 16:53:03.275914: 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": [] } ] -} +} \ No newline at end of file