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"<div align=\"center\">\n",
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"\n",
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" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
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" <img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_notebook.png\"></a>\n",
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"\n",
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"\n",
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"<br>\n",
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" <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
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" <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
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"<br>\n",
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"\n",
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"This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
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"\n",
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"</div>"
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]
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"source": [
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"# Setup\n",
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"\n",
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||
"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
|
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]
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},
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{
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"id": "wbvMlHd_QwMG",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "0f9ee467-cea4-48e8-9050-7a76ae1b6141"
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},
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"source": [
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"!git clone https://github.com/ultralytics/yolov5 # clone\n",
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"%cd yolov5\n",
|
||
"%pip install -qr requirements.txt # install\n",
|
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"\n",
|
||
"import torch\n",
|
||
"import utils\n",
|
||
"display = utils.notebook_init() # checks"
|
||
],
|
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"execution_count": 1,
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"outputs": [
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"output_type": "stream",
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"text": [
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"YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n"
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]
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},
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"text": [
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"Setup complete ✅ (8 CPUs, 51.0 GB RAM, 37.4/166.8 GB disk)\n"
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]
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}
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]
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},
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"source": [
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||
"# 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",
|
||
" path/ # directory\n",
|
||
" 'path/*.jpg' # glob\n",
|
||
" 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n",
|
||
" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
|
||
"```"
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]
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"outputId": "60647b99-e8d4-402c-f444-331bf6746da4"
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},
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"source": [
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"!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": 2,
|
||
"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\n",
|
||
"YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
|
||
"\n",
|
||
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...\n",
|
||
"100% 14.1M/14.1M [00:00<00:00, 27.8MB/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, 14.8ms\n",
|
||
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 20.1ms\n",
|
||
"Speed: 0.6ms pre-process, 17.4ms inference, 21.6ms 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",
|
||
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
|
||
]
|
||
},
|
||
{
|
||
"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/",
|
||
"height": 49,
|
||
"referenced_widgets": [
|
||
"9b8caa3522fc4cbab31e13b5dfc7808d",
|
||
"574140e4c4bc48c9a171541a02cd0211",
|
||
"35e03ce5090346c9ae602891470fc555",
|
||
"c942c208e72d46568b476bb0f2d75496",
|
||
"65881db1db8a4e9c930fab9172d45143",
|
||
"60b913d755b34d638478e30705a2dde1",
|
||
"0856bea36ec148b68522ff9c9eb258d8",
|
||
"76879f6f2aa54637a7a07faeea2bd684",
|
||
"0ace3934ec6f4d36a1b3a9e086390926",
|
||
"d6b7a2243e0c4beca714d99dceec23d6",
|
||
"5966ba6e6f114d8c9d8d1d6b1bd4f4c7"
|
||
]
|
||
},
|
||
"outputId": "102dabed-bc31-42fe-9133-d9ce28a2c01e"
|
||
},
|
||
"source": [
|
||
"# Download COCO val\n",
|
||
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
|
||
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
|
||
],
|
||
"execution_count": 3,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0.00/780M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "9b8caa3522fc4cbab31e13b5dfc7808d"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "X58w8JLpMnjH",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "daf60b1b-b098-4657-c863-584f4c9cf078"
|
||
},
|
||
"source": [
|
||
"# Validate YOLOv5s on COCO val\n",
|
||
"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
|
||
],
|
||
"execution_count": 4,
|
||
"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, 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 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
|
||
"\n",
|
||
"Fusing layers... \n",
|
||
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
|
||
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
|
||
"100% 755k/755k [00:00<00:00, 52.7MB/s]\n",
|
||
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10509.20it/s]\n",
|
||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
|
||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 157/157 [00:50<00:00, 3.10it/s]\n",
|
||
" all 5000 36335 0.67 0.521 0.566 0.371\n",
|
||
"Speed: 0.1ms pre-process, 1.0ms inference, 1.5ms 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=0.81s)\n",
|
||
"creating index...\n",
|
||
"index created!\n",
|
||
"Loading and preparing results...\n",
|
||
"DONE (t=5.62s)\n",
|
||
"creating index...\n",
|
||
"index created!\n",
|
||
"Running per image evaluation...\n",
|
||
"Evaluate annotation type *bbox*\n",
|
||
"DONE (t=77.03s).\n",
|
||
"Accumulating evaluation results...\n",
|
||
"DONE (t=14.63s).\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 ] = 0.724\n",
|
||
"Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ZY2VXXXu74w5"
|
||
},
|
||
"source": [
|
||
"# 3. Train\n",
|
||
"\n",
|
||
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png\"/></a></p>\n",
|
||
"Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
|
||
"<br><br>\n",
|
||
"\n",
|
||
"Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/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",
|
||
"<br><br>\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/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n",
|
||
"- Custom Training Notebook: [](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n",
|
||
"<br>\n",
|
||
"\n",
|
||
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
|
||
"logger = 'TensorBoard' #@param ['TensorBoard', 'ClearML', 'W&B']\n",
|
||
"\n",
|
||
"if logger == 'TensorBoard':\n",
|
||
" %load_ext tensorboard\n",
|
||
" %tensorboard --logdir runs/train\n",
|
||
"elif logger == 'ClearML':\n",
|
||
" %pip install -q clearml && clearml-init\n",
|
||
"elif logger == 'W&B':\n",
|
||
" %pip install -q wandb\n",
|
||
" import wandb; wandb.login()"
|
||
],
|
||
"metadata": {
|
||
"id": "i3oKtE4g-aNn"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "1NcFxRcFdJ_O",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "baa6d4be-3379-4aab-844a-d5a5396c0e49"
|
||
},
|
||
"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": 5,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\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 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\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[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
|
||
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\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://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
|
||
"100% 6.66M/6.66M [00:00<00:00, 41.1MB/s]\n",
|
||
"Dataset download success ✅ (0.8s), 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: 270 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' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 9659.25it/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, 951.31it/s]\n",
|
||
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 274.67it/s]\n",
|
||
"\n",
|
||
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
|
||
"Plotting labels to runs/train/exp/labels.jpg... \n",
|
||
"Image sizes 640 train, 640 val\n",
|
||
"Using 8 dataloader workers\n",
|
||
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
|
||
"Starting training for 3 epochs...\n",
|
||
"\n",
|
||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||
" 0/2 3.44G 0.04529 0.06712 0.01835 323 640: 100% 8/8 [00:04<00:00, 1.71it/s]\n",
|
||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.02it/s]\n",
|
||
" all 128 929 0.666 0.611 0.684 0.452\n",
|
||
"\n",
|
||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||
" 1/2 4.46G 0.04244 0.06423 0.01611 236 640: 100% 8/8 [00:01<00:00, 7.91it/s]\n",
|
||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.19it/s]\n",
|
||
" all 128 929 0.746 0.626 0.722 0.481\n",
|
||
"\n",
|
||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||
" 2/2 4.46G 0.04695 0.06875 0.0173 189 640: 100% 8/8 [00:00<00:00, 8.05it/s]\n",
|
||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.29it/s]\n",
|
||
" all 128 929 0.774 0.647 0.746 0.499\n",
|
||
"\n",
|
||
"3 epochs completed in 0.003 hours.\n",
|
||
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
|
||
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB\n",
|
||
"\n",
|
||
"Validating runs/train/exp/weights/best.pt...\n",
|
||
"Fusing layers... \n",
|
||
"Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
|
||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.21it/s]\n",
|
||
" all 128 929 0.774 0.647 0.746 0.499\n",
|
||
" person 128 254 0.87 0.697 0.806 0.534\n",
|
||
" bicycle 128 6 0.759 0.528 0.725 0.444\n",
|
||
" car 128 46 0.774 0.413 0.554 0.239\n",
|
||
" motorcycle 128 5 0.791 1 0.962 0.595\n",
|
||
" airplane 128 6 0.981 1 0.995 0.689\n",
|
||
" bus 128 7 0.65 0.714 0.755 0.691\n",
|
||
" train 128 3 1 0.573 0.995 0.602\n",
|
||
" truck 128 12 0.613 0.333 0.489 0.263\n",
|
||
" boat 128 6 0.933 0.333 0.507 0.209\n",
|
||
" traffic light 128 14 0.76 0.228 0.367 0.209\n",
|
||
" stop sign 128 2 0.821 1 0.995 0.821\n",
|
||
" bench 128 9 0.824 0.526 0.676 0.31\n",
|
||
" bird 128 16 0.974 1 0.995 0.611\n",
|
||
" cat 128 4 0.859 1 0.995 0.772\n",
|
||
" dog 128 9 1 0.666 0.883 0.647\n",
|
||
" horse 128 2 0.84 1 0.995 0.622\n",
|
||
" elephant 128 17 0.926 0.882 0.93 0.716\n",
|
||
" bear 128 1 0.709 1 0.995 0.995\n",
|
||
" zebra 128 4 0.866 1 0.995 0.922\n",
|
||
" giraffe 128 9 0.777 0.778 0.891 0.705\n",
|
||
" backpack 128 6 0.894 0.5 0.753 0.294\n",
|
||
" umbrella 128 18 0.876 0.783 0.899 0.54\n",
|
||
" handbag 128 19 0.799 0.209 0.335 0.179\n",
|
||
" tie 128 7 0.782 0.714 0.787 0.478\n",
|
||
" suitcase 128 4 0.658 1 0.945 0.581\n",
|
||
" frisbee 128 5 0.726 0.8 0.76 0.701\n",
|
||
" skis 128 1 0.8 1 0.995 0.103\n",
|
||
" snowboard 128 7 0.815 0.714 0.852 0.574\n",
|
||
" sports ball 128 6 0.649 0.667 0.602 0.307\n",
|
||
" kite 128 10 0.7 0.47 0.546 0.206\n",
|
||
" baseball bat 128 4 1 0.497 0.544 0.182\n",
|
||
" baseball glove 128 7 0.598 0.429 0.47 0.31\n",
|
||
" skateboard 128 5 0.851 0.6 0.685 0.495\n",
|
||
" tennis racket 128 7 0.754 0.429 0.544 0.34\n",
|
||
" bottle 128 18 0.564 0.333 0.53 0.264\n",
|
||
" wine glass 128 16 0.715 0.875 0.907 0.528\n",
|
||
" cup 128 36 0.825 0.639 0.803 0.535\n",
|
||
" fork 128 6 1 0.329 0.5 0.384\n",
|
||
" knife 128 16 0.706 0.625 0.666 0.405\n",
|
||
" spoon 128 22 0.836 0.464 0.619 0.379\n",
|
||
" bowl 128 28 0.763 0.607 0.717 0.516\n",
|
||
" banana 128 1 0.886 1 0.995 0.399\n",
|
||
" sandwich 128 2 1 0 0.62 0.546\n",
|
||
" orange 128 4 1 0.75 0.995 0.622\n",
|
||
" broccoli 128 11 0.548 0.443 0.467 0.35\n",
|
||
" carrot 128 24 0.7 0.585 0.699 0.458\n",
|
||
" hot dog 128 2 0.502 1 0.995 0.995\n",
|
||
" pizza 128 5 0.813 1 0.962 0.747\n",
|
||
" donut 128 14 0.662 1 0.96 0.838\n",
|
||
" cake 128 4 0.868 1 0.995 0.822\n",
|
||
" chair 128 35 0.538 0.571 0.594 0.322\n",
|
||
" couch 128 6 0.924 0.667 0.828 0.538\n",
|
||
" potted plant 128 14 0.731 0.786 0.824 0.495\n",
|
||
" bed 128 3 0.736 0.333 0.83 0.425\n",
|
||
" dining table 128 13 0.624 0.259 0.494 0.336\n",
|
||
" toilet 128 2 0.79 1 0.995 0.846\n",
|
||
" tv 128 2 0.574 1 0.995 0.796\n",
|
||
" laptop 128 3 1 0 0.695 0.367\n",
|
||
" mouse 128 2 1 0 0.173 0.0864\n",
|
||
" remote 128 8 1 0.62 0.634 0.557\n",
|
||
" cell phone 128 8 0.612 0.397 0.437 0.221\n",
|
||
" microwave 128 3 0.741 1 0.995 0.766\n",
|
||
" oven 128 5 0.33 0.4 0.449 0.3\n",
|
||
" sink 128 6 0.444 0.333 0.331 0.231\n",
|
||
" refrigerator 128 5 0.561 0.8 0.798 0.546\n",
|
||
" book 128 29 0.635 0.276 0.355 0.164\n",
|
||
" clock 128 9 0.766 0.889 0.888 0.73\n",
|
||
" vase 128 2 0.303 1 0.995 0.895\n",
|
||
" scissors 128 1 1 0 0.332 0.0397\n",
|
||
" teddy bear 128 21 0.842 0.508 0.739 0.499\n",
|
||
" toothbrush 128 5 0.787 1 0.928 0.59\n",
|
||
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "15glLzbQx5u0"
|
||
},
|
||
"source": [
|
||
"# 4. Visualize"
|
||
]
|
||
},
|
||
{
|
||
"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://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
|
||
"\n",
|
||
"<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
|
||
"<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
|
||
],
|
||
"metadata": {
|
||
"id": "Lay2WsTjNJzP"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "DLI1JmHU7B0l"
|
||
},
|
||
"source": [
|
||
"## Weights & Biases Logging\n",
|
||
"\n",
|
||
"[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
|
||
"\n",
|
||
"During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
|
||
"\n",
|
||
"<a href=\"https://wandb.ai/glenn-jocher/yolov5_tutorial\">\n",
|
||
"<img alt=\"Weights & Biases dashboard\" src=\"https://user-images.githubusercontent.com/26833433/182482859-288a9622-4661-48db-99de-650d1dead5c6.jpg\" width=\"1280\"/></a>"
|
||
]
|
||
},
|
||
{
|
||
"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",
|
||
"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\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",
|
||
"- **Google Colab and Kaggle** notebooks with free GPU: <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
|
||
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
|
||
"- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\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 for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "GMusP4OAxFu6"
|
||
},
|
||
"source": [
|
||
"import torch\n",
|
||
"\n",
|
||
"# PyTorch Hub Model\n",
|
||
"model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom\n",
|
||
"\n",
|
||
"# Images\n",
|
||
"img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list\n",
|
||
"\n",
|
||
"# Inference\n",
|
||
"results = model(img)\n",
|
||
"\n",
|
||
"# Results\n",
|
||
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "FGH0ZjkGjejy"
|
||
},
|
||
"source": [
|
||
"# YOLOv5 CI\n",
|
||
"%%shell\n",
|
||
"rm -rf runs # remove runs/\n",
|
||
"m=yolov5n # official weights\n",
|
||
"b=runs/train/exp/weights/best # best.pt checkpoint\n",
|
||
"python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0 # train\n",
|
||
"for d in 0 cpu; do # devices\n",
|
||
" for w in $m $b; do # weights\n",
|
||
" python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val\n",
|
||
" python detect.py --imgsz 64 --weights $w.pt --device $d # detect\n",
|
||
" done\n",
|
||
"done\n",
|
||
"python hubconf.py --model $m # hub\n",
|
||
"python models/tf.py --weights $m.pt # build TF model\n",
|
||
"python models/yolo.py --cfg $m.yaml # build PyTorch model\n",
|
||
"python export.py --weights $m.pt --img 64 --include torchscript # export"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "mcKoSIK2WSzj"
|
||
},
|
||
"source": [
|
||
"# Reproduce\n",
|
||
"for x in (f'yolov5{x}' for x in 'nsmlx'):\n",
|
||
" !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed\n",
|
||
" !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "gogI-kwi3Tye"
|
||
},
|
||
"source": [
|
||
"# Profile\n",
|
||
"from utils.torch_utils import profile\n",
|
||
"\n",
|
||
"m1 = lambda x: x * torch.sigmoid(x)\n",
|
||
"m2 = torch.nn.SiLU()\n",
|
||
"results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "BSgFCAcMbk1R"
|
||
},
|
||
"source": [
|
||
"# VOC\n",
|
||
"for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']): # batch, model\n",
|
||
" !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Classification train\n",
|
||
"for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'resnet101.pt', 'efficientnet_b0.pt', 'efficientnet_b1.pt']:\n",
|
||
" for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':\n",
|
||
" !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}"
|
||
],
|
||
"metadata": {
|
||
"id": "UWGH7H6yakVl"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Classification val\n",
|
||
"!bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G - 50000 images)\n",
|
||
"!python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate"
|
||
],
|
||
"metadata": {
|
||
"id": "yYgOiFNHZx-1"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794\n",
|
||
"!bash data/scripts/get_coco.sh --test # download COCO test-dev2017 (7G - 40000 images, test 20000)\n",
|
||
"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
|
||
],
|
||
"metadata": {
|
||
"id": "aq4DPWGu0Bl1"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"id": "VTRwsvA9u7ln"
|
||
},
|
||
"source": [
|
||
"# TensorRT \n",
|
||
"!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # install\n",
|
||
"!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0 # export\n",
|
||
"!python detect.py --weights yolov5s.engine --imgsz 640 --device 0 # inference"
|
||
],
|
||
"execution_count": null,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |