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ultralytics 8.0.65 YOLOv8 Pose models (#1347)

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Ayush Chaurasia
2023-04-06 03:55:32 +05:30
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examples/tutorial.ipynb
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@ -32,11 +32,11 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
"<br>\n",
"\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) object detection and image segmentation model developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
"\n",
"The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks. They can be trained on large datasets and are capable of running on a variety of hardware platforms, from CPUs to GPUs.\n",
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
"\n",
"Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources in this notebook will help you get the most out of YOLOv8. Please feel free to browse the <a href=\"https://docs.ultralytics.com/\">YOLOv8 Docs</a> and reach out to us with any questions or feedback.\n",
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://discord.gg/n6cFeSPZdD\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@ -66,7 +66,7 @@
"import ultralytics\n",
"ultralytics.checks()"
],
"execution_count": 1,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -86,7 +86,7 @@
"source": [
"# 1. Predict\n",
"\n",
"YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
"YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) and other details in the [YOLOv8 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
]
},
{
@ -102,7 +102,7 @@
"# Run inference on an image with YOLOv8n\n",
"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
],
"execution_count": 3,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -135,7 +135,7 @@
},
"source": [
"# 2. Val\n",
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used."
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
]
},
{
@ -165,7 +165,7 @@
"# Validate YOLOv8n on COCO128 val\n",
"!yolo val model=yolov8n.pt data=coco128.yaml"
],
"execution_count": 4,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -273,7 +273,7 @@
"\n",
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
"\n",
"Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/) and [Classification](https://docs.ultralytics.com/tasks/classify/) datasets."
"Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
]
},
{
@ -289,7 +289,7 @@
"# Train YOLOv8n on COCO128 for 3 epochs\n",
"!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
],
"execution_count": 5,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -449,7 +449,7 @@
"source": [
"# 4. Export\n",
"\n",
"Export a YOLOv8 model to any supported format with the `format` argument, i.e. `format=onnx`.\n",
"Export a YOLOv8 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
"\n",
"- 💡 ProTip: Export to [ONNX](https://onnx.ai/) or [OpenVINO](https://docs.openvino.ai/latest/index.html) for up to 3x CPU speedup. \n",
"- 💡 ProTip: Export to [TensorRT](https://developer.nvidia.com/tensorrt) for up to 5x GPU speedup.\n",
@ -487,7 +487,7 @@
"id": "CYIjW4igCjqD",
"outputId": "49b5bb9d-2c16-415b-c3e7-ec95c15a9e62"
},
"execution_count": 6,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -515,7 +515,7 @@
"source": [
"# 5. Python Usage\n",
"\n",
"YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com/usage/python/)."
"YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/python/)."
],
"metadata": {
"id": "kUMOQ0OeDBJG"
@ -547,7 +547,7 @@
"source": [
"# 6. Tasks\n",
"\n",
"YOLOv8 can train, val, predict and export models for the 3 primary tasks in vision AI: detection, segmentation and classification.\n",
"YOLOv8 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
"\n",
"<img width=\"1024\" src=\"https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png\">\n"
],
@ -636,6 +636,33 @@
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 4. Pose\n",
"\n",
"YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
],
"metadata": {
"id": "SpIaFLiO11TG"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n classification model\n",
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
"metadata": {
"id": "si4aKFNg19vX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {