diff --git a/tutorial.ipynb b/tutorial.ipynb index 3f7133f4f7d7..7587d9f536fe 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -16,7 +16,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "811fd52fef65422c8267bafcde8a2c3d": { + "1f8e9b8ebded4175b2eaa9f75c3ceb00": { "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_8f41b90117224eef9133a9c3a103dbba", + "layout": "IPY_MODEL_0a1246a73077468ab80e979cc0576cd2", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_ca2fb37af6ed43d4a74cdc9f2ac5c4a5", - "IPY_MODEL_29419ae5ebb9403ea73f7e5a68037bdd" + "IPY_MODEL_d327cde5a85a4a51bb8b1b3e9cf06c97", + "IPY_MODEL_d5ef1cb2cbed4b87b3c5d292ff2b0da6" ] } }, - "8f41b90117224eef9133a9c3a103dbba": { + "0a1246a73077468ab80e979cc0576cd2": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -87,12 +87,12 @@ "left": null } }, - "ca2fb37af6ed43d4a74cdc9f2ac5c4a5": { + "d327cde5a85a4a51bb8b1b3e9cf06c97": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_6511b4dfb10b48d1bc98bcfb3987bfa0", + "style": "IPY_MODEL_8d5dff8bca14435a88fa1814533acd85", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -107,30 +107,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_64f0badf1a8f489885aa984dd62d37dc" + "layout": "IPY_MODEL_3d5136c19e7645ca9bc8f51ceffb2be1" } }, - "29419ae5ebb9403ea73f7e5a68037bdd": { + "d5ef1cb2cbed4b87b3c5d292ff2b0da6": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_f569911c5cfc4d81bb1bdfa83447afc8", + "style": "IPY_MODEL_2919396dbd4b4c8e821d12bd28665d8a", "_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, 34.2MB/s]", + "value": " 781M/781M [00:12<00:00, 65.5MB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_84943ade566440aaa2dcf3b3b27e7074" + "layout": "IPY_MODEL_6feb16f2b2fa4021b1a271e1dd442d04" } }, - "6511b4dfb10b48d1bc98bcfb3987bfa0": { + "8d5dff8bca14435a88fa1814533acd85": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -145,7 +145,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "64f0badf1a8f489885aa984dd62d37dc": { + "3d5136c19e7645ca9bc8f51ceffb2be1": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -196,7 +196,7 @@ "left": null } }, - "f569911c5cfc4d81bb1bdfa83447afc8": { + "2919396dbd4b4c8e821d12bd28665d8a": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -210,7 +210,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "84943ade566440aaa2dcf3b3b27e7074": { + "6feb16f2b2fa4021b1a271e1dd442d04": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -261,7 +261,7 @@ "left": null } }, - "8501ed1563e4452eac9df6b7a66e8f8c": { + "e6459e0bcee449b090fc9807672725bc": { "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_d2bb96801e1f46f4a58e02534f7026ff", + "layout": "IPY_MODEL_c341e1d3bf3b40d1821ce392eb966c68", "_model_module": "@jupyter-widgets/controls", "children": [ - "IPY_MODEL_468a796ef06b4a24bcba6fbd4a0a8db5", - "IPY_MODEL_42ad5c1ea7be4835bffebf90642178f1" + "IPY_MODEL_660afee173694231a6dce3cd94df6cae", + "IPY_MODEL_261218485cef48df961519dde5edfcbe" ] } }, - "d2bb96801e1f46f4a58e02534f7026ff": { + "c341e1d3bf3b40d1821ce392eb966c68": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -332,12 +332,12 @@ "left": null } }, - "468a796ef06b4a24bcba6fbd4a0a8db5": { + "660afee173694231a6dce3cd94df6cae": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "state": { "_view_name": "ProgressView", - "style": "IPY_MODEL_c58b5536d98f4814831934e9c30c4d78", + "style": "IPY_MODEL_32736d503c06497abfae8c0421918255", "_dom_classes": [], "description": "100%", "_model_name": "FloatProgressModel", @@ -352,30 +352,30 @@ "min": 0, "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_505597101151486ea29e9ab754544d27" + "layout": "IPY_MODEL_e257738711f54d5280c8393d9d3dce1c" } }, - "42ad5c1ea7be4835bffebf90642178f1": { + "261218485cef48df961519dde5edfcbe": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_view_name": "HTMLView", - "style": "IPY_MODEL_de6e7b4b4a1c408c9f89d89b07a13bcd", + "style": "IPY_MODEL_beb7a6fe34b840899bb79c062681696f", "_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, 18.2MB/s]", + "value": " 21.1M/21.1M [00:00<00:00, 33.5MB/s]", "_view_count": null, "_view_module_version": "1.5.0", "description_tooltip": null, "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_f5cc9c7d4c274b2d81327ba3163c43fd" + "layout": "IPY_MODEL_e639132395d64d70b99d8b72c32f8fbb" } }, - "c58b5536d98f4814831934e9c30c4d78": { + "32736d503c06497abfae8c0421918255": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "state": { @@ -390,7 +390,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "505597101151486ea29e9ab754544d27": { + "e257738711f54d5280c8393d9d3dce1c": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -441,7 +441,7 @@ "left": null } }, - "de6e7b4b4a1c408c9f89d89b07a13bcd": { + "beb7a6fe34b840899bb79c062681696f": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "state": { @@ -455,7 +455,7 @@ "_model_module": "@jupyter-widgets/controls" } }, - "f5cc9c7d4c274b2d81327ba3163c43fd": { + "e639132395d64d70b99d8b72c32f8fbb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { @@ -550,7 +550,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "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": [ { "output_type": "stream", "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" ], "name": "stdout" } @@ -672,30 +672,30 @@ "base_uri": "https://localhost:8080/", "height": 65, "referenced_widgets": [ - "811fd52fef65422c8267bafcde8a2c3d", - "8f41b90117224eef9133a9c3a103dbba", - "ca2fb37af6ed43d4a74cdc9f2ac5c4a5", - "29419ae5ebb9403ea73f7e5a68037bdd", - "6511b4dfb10b48d1bc98bcfb3987bfa0", - "64f0badf1a8f489885aa984dd62d37dc", - "f569911c5cfc4d81bb1bdfa83447afc8", - "84943ade566440aaa2dcf3b3b27e7074" + "1f8e9b8ebded4175b2eaa9f75c3ceb00", + "0a1246a73077468ab80e979cc0576cd2", + "d327cde5a85a4a51bb8b1b3e9cf06c97", + "d5ef1cb2cbed4b87b3c5d292ff2b0da6", + "8d5dff8bca14435a88fa1814533acd85", + "3d5136c19e7645ca9bc8f51ceffb2be1", + "2919396dbd4b4c8e821d12bd28665d8a", + "6feb16f2b2fa4021b1a271e1dd442d04" ] }, - "outputId": "59a7a546-8492-492e-861d-70a2c85a6794" + "outputId": "d6ace7c6-1be5-41ff-d607-1c716b88d298" }, "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": "811fd52fef65422c8267bafcde8a2c3d", + "model_id": "1f8e9b8ebded4175b2eaa9f75c3ceb00", "version_minor": 0, "version_major": 2 }, @@ -723,46 +723,45 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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", @@ -837,30 +836,30 @@ "base_uri": "https://localhost:8080/", "height": 65, "referenced_widgets": [ - "8501ed1563e4452eac9df6b7a66e8f8c", - "d2bb96801e1f46f4a58e02534f7026ff", - "468a796ef06b4a24bcba6fbd4a0a8db5", - "42ad5c1ea7be4835bffebf90642178f1", - "c58b5536d98f4814831934e9c30c4d78", - "505597101151486ea29e9ab754544d27", - "de6e7b4b4a1c408c9f89d89b07a13bcd", - "f5cc9c7d4c274b2d81327ba3163c43fd" + "e6459e0bcee449b090fc9807672725bc", + "c341e1d3bf3b40d1821ce392eb966c68", + "660afee173694231a6dce3cd94df6cae", + "261218485cef48df961519dde5edfcbe", + "32736d503c06497abfae8c0421918255", + "e257738711f54d5280c8393d9d3dce1c", + "beb7a6fe34b840899bb79c062681696f", + "e639132395d64d70b99d8b72c32f8fbb" ] }, - "outputId": "c68a3db4-1314-46b4-9e52-83532eb65749" + "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", + "model_id": "e6459e0bcee449b090fc9807672725bc", "version_minor": 0, "version_major": 2 }, @@ -925,27 +924,27 @@ "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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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 mAP@.5 mAP@.5:.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": [] } ] -} +} \ No newline at end of file