diff --git a/classify/tutorial.ipynb b/classify/tutorial.ipynb
index c547a29..d5433f8 100644
--- a/classify/tutorial.ipynb
+++ b/classify/tutorial.ipynb
@@ -7,19 +7,41 @@
},
"source": [
"\n",
+ "
\n",
+ " ![](\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\")
\n",
+ " \n",
"\n",
- "
\n",
- " \n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n",
- "\n",
- "
\n",
- "
![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\")
\n",
+ "
![\"Ultralytics](\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\")
\n",
+ "
\n",
"
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
\n",
- "
![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ "
\n",
+ "\n",
+ "
![\"Discord\"](\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\")
\n",
+ "
![\"Ultralytics](\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\")
\n",
+ "
![\"Ultralytics](\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\")
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " ![\"Ultralytics](\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\")
\n",
+ " \n",
"\n",
- "This
YOLOv5 🚀 notebook by
Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See
GitHub for community support or
contact us for professional support.\n",
- "\n",
+ "
\n",
+ " Watch: How to Train\n",
+ " Ultralytics\n",
+ " YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\n",
+ "
\n",
"
![](\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/)
\n",
- "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
- "
\n",
+ "![](\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/)
\n",
"\n",
"Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\n",
"\n",
@@ -1235,17 +1255,7 @@
"- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\n",
"
\n",
"\n",
- "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
- "\n",
- "## Train on Custom Data with Roboflow 🌟 NEW\n",
- "\n",
- "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
- "\n",
- "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-classification-custom-data/](https://blog.roboflow.com/train-yolov5-classification-custom-data/?ref=ultralytics)\n",
- "- Custom Training Notebook: [](https://colab.research.google.com/drive/1KZiKUAjtARHAfZCXbJRv14-pOnIsBLPV?usp=sharing)\n",
- "
\n",
- "\n",
- "![](\"https://user-images.githubusercontent.com/26833433/202802162-92e60571-ab58-4409-948d-b31fddcd3c6f.png\"/)
Label images lightning fast (including with model-assisted labeling)"
+ "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic."
]
},
{
@@ -1289,24 +1299,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
+ "\u001B[34m\u001B[1mclassify/train: \u001B[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
+ "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
- "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
+ "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
"\n",
"Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n",
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n",
"100% 103M/103M [00:00<00:00, 347MB/s] \n",
"Unzipping /content/datasets/imagenette160.zip...\n",
- "Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n",
+ "Dataset download success ✅ (3.3s), saved to \u001B[1m/content/datasets/imagenette160\u001B[0m\n",
"\n",
- "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
+ "\u001B[34m\u001B[1malbumentations: \u001B[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
"Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n",
- "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n",
+ "\u001B[34m\u001B[1moptimizer:\u001B[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n",
"Image sizes 224 train, 224 test\n",
"Using 1 dataloader workers\n",
- "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n",
+ "Logging results to \u001B[1mruns/train-cls/exp\u001B[0m\n",
"Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n",
"\n",
" Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n",
@@ -1317,7 +1327,7 @@
" 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\n",
"\n",
"Training complete (0.052 hours)\n",
- "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n",
+ "Results saved to \u001B[1mruns/train-cls/exp\u001B[0m\n",
"Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n",
"Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n",
"Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n",
diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb
index bb5c1f9..7575dc1 100644
--- a/segment/tutorial.ipynb
+++ b/segment/tutorial.ipynb
@@ -7,19 +7,41 @@
},
"source": [
"\n",
+ "
\n",
+ " \n",
+ " \n",
"\n",
- "
\n",
- " \n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n",
- "\n",
- "
\n",
- "
\n",
+ "
\n",
+ "
\n",
"
\n",
- "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
"\n",
- "This
YOLOv5 🚀 notebook by
Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See
GitHub for community support or
contact us for professional support.\n",
- "\n",
+ "
\n",
+ " Watch: How to Train\n",
+ " Ultralytics\n",
+ " YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\n",
+ "
\n",
"
\n",
- "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
- "
\n",
+ "
\n",
"\n",
"Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n",
"\n",
@@ -230,17 +250,7 @@
"- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n",
"
\n",
"\n",
- "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
- "\n",
- "## Train on Custom Data with Roboflow 🌟 NEW\n",
- "\n",
- "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
- "\n",
- "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n",
- "- Custom Training Notebook: [](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n",
- "
\n",
- "\n",
- "![](\"https://robflow-public-assets.s3.amazonaws.com/how-to-train-yolov5-segmentation-annotation.gif\"/)
Label images lightning fast (including with model-assisted labeling)"
+ "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic."
]
},
{
@@ -284,17 +294,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
+ "\u001B[34m\u001B[1msegment/train: \u001B[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
+ "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
- "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
+ "\u001B[34m\u001B[1mhyperparameters: \u001B[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
+ "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
"\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n",
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip to coco128-seg.zip...\n",
"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n",
- "Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
+ "Dataset download success ✅ (1.9s), saved to \u001B[1m/content/datasets\u001B[0m\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
@@ -325,20 +335,20 @@
"Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n",
"\n",
"Transferred 367/367 items from yolov5s-seg.pt\n",
- "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
- "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n",
- "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "This **Ultralytics YOLOv5 Classification Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\n",
+ "\n",
+ "Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\n",
+ "\n",
+ "Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\n",
+ "\n",
+ "Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n",
+ "\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ "\n",
+ "
\n",
+ " Watch: How to Train\n",
+ " Ultralytics\n",
+ " YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀\n",
+ "
\n",
+ "
\n",
- "\n",
- "
\n",
- " \n",
- "\n",
- "[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [العربية](https://docs.ultralytics.com/ar/)\n",
- "\n",
- "
\n",
- "
\n",
- "
\n",
- "\n",
- "This
YOLOv5 🚀 notebook by
Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5
Docs for details, raise an issue on
GitHub for support, and join our
Discord community for questions and discussions!\n",
- "\n",
- "
![](\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0eq1SMWl6Sfn"
- },
- "source": [
- "# 2. Validate\n",
- "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "WQPtK1QYVaD_",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
- },
- "source": [
- "# Download COCO val\n",
- "torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
- "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "X58w8JLpMnjH",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
- },
- "source": [
- "# Validate YOLOv5s on COCO val\n",
- "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
- "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
- "\n",
- "Fusing layers... \n",
- "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
- " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n",
- " all 5000 36335 0.671 0.519 0.566 0.371\n",
- "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
- "\n",
- "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
- "loading annotations into memory...\n",
- "Done (t=0.43s)\n",
- "creating index...\n",
- "index created!\n",
- "Loading and preparing results...\n",
- "DONE (t=5.32s)\n",
- "creating index...\n",
- "index created!\n",
- "Running per image evaluation...\n",
- "Evaluate annotation type *bbox*\n",
- "DONE (t=78.89s).\n",
- "Accumulating evaluation results...\n",
- "DONE (t=14.51s).\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
- " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
- " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
- " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
- " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
- "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ZY2VXXXu74w5"
- },
- "source": [
- "# 3. Train\n",
- "\n",
- "
\n",
- "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
- "
\n",
- "\n",
- "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
- "\n",
- "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
- "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
- "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
- "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
- "
\n",
- "\n",
- "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
- "\n",
- "## Label a dataset on Roboflow (optional)\n",
- "\n",
- "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package."
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
- "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
- "\n",
- "if logger == 'Comet':\n",
- " %pip install -q comet_ml\n",
- " import comet_ml; comet_ml.init()\n",
- "elif logger == 'ClearML':\n",
- " %pip install -q clearml\n",
- " import clearml; clearml.browser_login()\n",
- "elif logger == 'TensorBoard':\n",
- " %load_ext tensorboard\n",
- " %tensorboard --logdir runs/train"
- ],
- "metadata": {
- "id": "i3oKtE4g-aNn"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "1NcFxRcFdJ_O",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a"
- },
- "source": [
- "# Train YOLOv5s on COCO128 for 3 epochs\n",
- "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
- "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
- "2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
- "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
- "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
- "\n",
- "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
- "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
- "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
- "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
- "\n",
- "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
- "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...\n",
- "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n",
- "Dataset download success ✅ (0.6s), saved to \u001b[1m/content/datasets\u001b[0m\n",
- "\n",
- " from n params module arguments \n",
- " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \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 2 115712 models.common.C3 [128, 128, 2] \n",
- " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
- " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
- " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
- " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
- " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \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: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
- "\n",
- "Transferred 349/349 items from yolov5s.pt\n",
- "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
- "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
- "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n",
- "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n",
- "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
- "```\n",
- "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
- "[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
- "\n",
- "\n",
- "![\"Comet](\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\")
"
- ],
- "metadata": {
- "id": "nWOsI5wJR1o3"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "## ClearML Logging and Automation 🌟 NEW\n",
- "\n",
- "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
- "\n",
- "- `pip install clearml`\n",
- "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
- "\n",
- "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
- "\n",
- "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
- "\n",
- "\n",
- "![\"ClearML](\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\")
"
- ],
- "metadata": {
- "id": "Lay2WsTjNJzP"
- }
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-WPvRbS5Swl6"
- },
- "source": [
- "## Local Logging\n",
- "\n",
- "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
- "\n",
- "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
- "\n",
- "![\"Local](\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\")
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Zelyeqbyt3GD"
- },
- "source": [
- "# Environments\n",
- "\n",
- "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
- "\n",
- "- **Notebooks** with free GPU: \n",
- "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
- "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
- "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6Qu7Iesl0p54"
- },
- "source": [
- "# Status\n",
- "\n",
- "\n",
- "\n",
- "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IEijrePND_2I"
- },
- "source": [
- "# Appendix\n",
- "\n",
- "Additional content below."
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "GMusP4OAxFu6"
- },
- "source": [
- "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
- "import torch\n",
- "\n",
- "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n",
- "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
- "results = model(im) # inference\n",
- "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
- ],
- "execution_count": null,
- "outputs": []
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n"
+ ]
}
- ]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4JnkELT0cIJg"
+ },
+ "source": [
+ "# 1. Detect\n",
+ "\n",
+ "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
+ "\n",
+ "```shell\n",
+ "python detect.py --source 0 # webcam\n",
+ " img.jpg # image\n",
+ " vid.mp4 # video\n",
+ " screen # screenshot\n",
+ " path/ # directory\n",
+ " 'path/*.jpg' # glob\n",
+ " 'https://youtu.be/LNwODJXcvt4' # YouTube\n",
+ " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zR9ZbuQCH7FX",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "284ef04b-1596-412f-88f6-948828dd2b49"
+ },
+ "source": [
+ "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
+ "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001B[34m\u001B[1mdetect: \u001B[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
+ "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
+ "\n",
+ "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
+ "100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\n",
+ "\n",
+ "Fusing layers... \n",
+ "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
+ "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n",
+ "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n",
+ "Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n",
+ "Results saved to \u001B[1mruns/detect/exp\u001B[0m\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hkAzDWJ7cWTr"
+ },
+ "source": [
+ " \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WQPtK1QYVaD_",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
+ },
+ "source": [
+ "# Download COCO val\n",
+ "torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
+ "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "X58w8JLpMnjH",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
+ },
+ "source": [
+ "# Validate YOLOv5s on COCO val\n",
+ "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001B[34m\u001B[1mval: \u001B[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
+ "\u001B[34m\u001B[1mval: \u001B[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
+ "\u001B[34m\u001B[1mval: \u001B[0mNew cache created: /content/datasets/coco/val2017.cache\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n",
+ " all 5000 36335 0.671 0.519 0.566 0.371\n",
+ "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
+ "\n",
+ "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.43s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loading and preparing results...\n",
+ "DONE (t=5.32s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Running per image evaluation...\n",
+ "Evaluate annotation type *bbox*\n",
+ "DONE (t=78.89s).\n",
+ "Accumulating evaluation results...\n",
+ "DONE (t=14.51s).\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
+ " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
+ " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
+ "Results saved to \u001B[1mruns/val/exp\u001B[0m\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "
\n",
+ "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
+ "
\n",
+ "\n",
+ "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
+ "\n",
+ "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
+ "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
+ "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
+ "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
+ "
\n",
+ "\n",
+ "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
+ "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
+ "\n",
+ "if logger == 'Comet':\n",
+ " %pip install -q comet_ml\n",
+ " import comet_ml; comet_ml.init()\n",
+ "elif logger == 'ClearML':\n",
+ " %pip install -q clearml\n",
+ " import clearml; clearml.browser_login()\n",
+ "elif logger == 'TensorBoard':\n",
+ " %load_ext tensorboard\n",
+ " %tensorboard --logdir runs/train"
+ ],
+ "metadata": {
+ "id": "i3oKtE4g-aNn"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1NcFxRcFdJ_O",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a"
+ },
+ "source": [
+ "# Train YOLOv5s on COCO128 for 3 epochs\n",
+ "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+ "\u001B[34m\u001B[1mtrain: \u001B[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
+ "\u001B[34m\u001B[1mgithub: \u001B[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
+ "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
+ "\n",
+ "\u001B[34m\u001B[1mhyperparameters: \u001B[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
+ "\u001B[34m\u001B[1mClearML: \u001B[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
+ "\u001B[34m\u001B[1mComet: \u001B[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
+ "\u001B[34m\u001B[1mTensorBoard: \u001B[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
+ "\n",
+ "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
+ "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...\n",
+ "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n",
+ "Dataset download success ✅ (0.6s), saved to \u001B[1m/content/datasets\u001B[0m\n",
+ "\n",
+ " from n params module arguments \n",
+ " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \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 2 115712 models.common.C3 [128, 128, 2] \n",
+ " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
+ " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
+ " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
+ " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
+ " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \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: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
+ "\n",
+ "Transferred 349/349 items from yolov5s.pt\n",
+ "\u001B[34m\u001B[1mAMP: \u001B[0mchecks passed ✅\n",
+ "\u001B[34m\u001B[1moptimizer:\u001B[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
+ "\u001B[34m\u001B[1malbumentations: \u001B[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
+ "\u001B[34m\u001B[1mtrain: \u001B[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n",
+ "\u001B[34m\u001B[1mtrain: \u001B[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
+ "\u001B[34m\u001B[1mtrain: \u001B[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n",
+ "\u001B[34m\u001B[1mval: \u001B[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n",
+ "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
+ "```\n",
+ "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
+ "[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "nWOsI5wJR1o3"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "import torch\n",
+ "\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n",
+ "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
}