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--- |
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title: YOLO |
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app_file: demo/hf_demo.py |
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sdk: gradio |
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sdk_version: 4.36.1 |
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--- |
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# YOLO: Official Implementation of YOLOv9, YOLOv7 |
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[](https://yolo-docs.readthedocs.io/en/latest/?badge=latest) |
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[](https://paperswithcode.com/sota/real-time-object-detection-on-coco) |
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[]() |
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[](https://huggingface.co/spaces/henry000/YOLO) |
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<!-- > [!IMPORTANT] |
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> This project is currently a Work In Progress and may undergo significant changes. It is not recommended for use in production environments until further notice. Please check back regularly for updates. |
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> |
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> Use of this code is at your own risk and discretion. It is advisable to consult with the project owner before deploying or integrating into any critical systems. --> |
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Welcome to the official implementation of YOLOv7 and YOLOv9. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9. |
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## TL;DR |
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- This is the official YOLO model implementation with an MIT License. |
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- For quick deployment: you can directly install by pip+git: |
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```shell |
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pip install git+https://github.com/WongKinYiu/YOLO.git |
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yolo task.data.source=0 # source could be a single file, video, image folder, webcam ID |
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``` |
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## Introduction |
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- [**YOLOv9**: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616) |
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- [**YOLOv7**: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors](https://arxiv.org/abs/2207.02696) |
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## Installation |
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To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies: |
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```shell |
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git clone git@github.com:WongKinYiu/YOLO.git |
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cd YOLO |
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pip install -r requirements.txt |
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``` |
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## Features |
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<table> |
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<tr><td> |
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| Tools | pip 🐍 | HuggingFace 🤗 | Docker 🐳 | |
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| -------------------- | :----: | :--------------: | :-------: | |
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| Compatibility | ✅ | ✅ | 🧪 | |
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| Phase | Training | Validation | Inference | |
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| ------------------- | :------: | :---------: | :-------: | |
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| Supported | ✅ | ✅ | ✅ | |
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</td><td> |
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| Device | CUDA | CPU | MPS | |
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| ------------------ | :---------: | :-------: | :-------: | |
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| PyTorch | v1.12 | v2.3+ | v1.12 | |
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| ONNX | ✅ | ✅ | - | |
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| TensorRT | ✅ | - | - | |
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| OpenVINO | - | 🧪 | ❔ | |
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</td></tr> </table> |
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## Task |
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These are simple examples. For more customization details, please refer to [Notebooks](examples) and lower-level modifications **[HOWTO](docs/HOWTO.md)**. |
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## Training |
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To train YOLO on your machine/dataset: |
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1. Modify the configuration file `yolo/config/dataset/**.yaml` to point to your dataset. |
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2. Run the training script: |
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```shell |
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python yolo/lazy.py task=train dataset=** use_wandb=True |
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python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c weight=False # or more args |
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``` |
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### Transfer Learning |
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To perform transfer learning with YOLOv9: |
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```shell |
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python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda} |
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``` |
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### Inference |
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To use a model for object detection, use: |
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```shell |
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python yolo/lazy.py # if cloned from GitHub |
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python yolo/lazy.py task=inference \ # default is inference |
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name=AnyNameYouWant \ # AnyNameYouWant |
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device=cpu \ # hardware cuda, cpu, mps |
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model=v9-s \ # model version: v9-c, m, s |
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task.nms.min_confidence=0.1 \ # nms config |
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task.fast_inference=onnx \ # onnx, trt, deploy |
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task.data.source=data/toy/images/train \ # file, dir, webcam |
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+quite=True \ # Quite Output |
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yolo task.data.source={Any Source} # if pip installed |
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yolo task=inference task.data.source={Any} |
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``` |
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### Validation |
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To validate model performance, or generate a json file in COCO format: |
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```shell |
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python yolo/lazy.py task=validation |
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python yolo/lazy.py task=validation dataset=toy |
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``` |
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## Contributing |
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Contributions to the YOLO project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for guidelines on how to contribute. |
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### TODO Diagrams |
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```mermaid |
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flowchart TB |
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subgraph Features |
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Taskv7-->Segmentation["#35 Segmentation"] |
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Taskv7-->Classification["#34 Classification"] |
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Taskv9-->Segmentation |
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Taskv9-->Classification |
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Trainv7 |
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end |
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subgraph Model |
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MODELv7-->v7-X |
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MODELv7-->v7-E6 |
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MODELv7-->v7-E6E |
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MODELv9-->v9-T |
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MODELv9-->v9-S |
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MODELv9-->v9-E |
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end |
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subgraph Bugs |
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Fix-->Fix1["#12 mAP > 1"] |
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Fix-->Fix2["v9 Gradient Bump"] |
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Reply-->Reply1["#39"] |
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Reply-->Reply2["#36"] |
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end |
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``` |
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## Star History |
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[](https://star-history.com/#WongKinYiu/YOLO&Date) |
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## Citations |
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``` |
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@misc{wang2022yolov7, |
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title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, |
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author={Chien-Yao Wang and Alexey Bochkovskiy and Hong-Yuan Mark Liao}, |
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year={2022}, |
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eprint={2207.02696}, |
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archivePrefix={arXiv}, |
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primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'} |
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} |
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@misc{wang2024yolov9, |
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title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information}, |
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author={Chien-Yao Wang and I-Hau Yeh and Hong-Yuan Mark Liao}, |
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year={2024}, |
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eprint={2402.13616}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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