https://github.com/MultimediaTechLab/YOLO
An MIT License of YOLOv9, YOLOv7, YOLO-RD
https://github.com/MultimediaTechLab/YOLO
Last synced: 2 months ago
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An MIT License of YOLOv9, YOLOv7, YOLO-RD
- Host: GitHub
- URL: https://github.com/MultimediaTechLab/YOLO
- Owner: MultimediaTechLab
- License: mit
- Created: 2024-03-14T00:06:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-06T08:10:07.000Z (2 months ago)
- Last Synced: 2025-02-06T08:38:13.211Z (2 months ago)
- Language: Python
- Homepage: https://yolo-docs.readthedocs.io/en/latest/?badge=latest
- Size: 1.53 MB
- Stars: 981
- Watchers: 43
- Forks: 119
- Open Issues: 96
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - MultimediaTechLab/YOLO - RD. Welcome to the official implementation of YOLOv7 and YOLOv9, YOLO-RD. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9. (Summary)
README
# YOLO: Official Implementation of YOLOv9, YOLOv7, YOLO-RD
[](https://yolo-docs.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/WongKinYiu/YOLO/actions/workflows/develop.yaml)
[](https://github.com/WongKinYiu/YOLO/actions/workflows/deploy.yaml)
[](https://paperswithcode.com/sota/real-time-object-detection-on-coco)
[]()
[](https://huggingface.co/spaces/henry000/YOLO)
Welcome to the official implementation of YOLOv7 and YOLOv9, YOLO-RD. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.
## TL;DR
- This is the official YOLO model implementation with an MIT License.
- For quick deployment: you can directly install by pip+git:
```shell
pip install git+https://github.com/WongKinYiu/YOLO.git
yolo task.data.source=0 # source could be a single file, video, image folder, webcam ID
```
## Introduction
- [**YOLOv9**: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
- [**YOLOv7**: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors](https://arxiv.org/abs/2207.02696)
- [**YOLO-RD**: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary](https://arxiv.org/abs/2410.15346)
## Installation
To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:
```shell
git clone [email protected]:WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements.txt
```
## Features
## Task
These are simple examples. For more customization details, please refer to [Notebooks](examples) and lower-level modifications **[HOWTO](docs/HOWTO.md)**.
## Training
To train YOLO on your machine/dataset:
1. Modify the configuration file `yolo/config/dataset/**.yaml` to point to your dataset.
2. Run the training script:
```shell
python yolo/lazy.py task=train dataset=** use_wandb=True
python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c weight=False # or more args
```
### Transfer Learning
To perform transfer learning with YOLOv9:
```shell
python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda}
```
### Inference
To use a model for object detection, use:
```shell
python yolo/lazy.py # if cloned from GitHub
python yolo/lazy.py task=inference \ # default is inference
name=AnyNameYouWant \ # AnyNameYouWant
device=cpu \ # hardware cuda, cpu, mps
model=v9-s \ # model version: v9-c, m, s
task.nms.min_confidence=0.1 \ # nms config
task.fast_inference=onnx \ # onnx, trt, deploy
task.data.source=data/toy/images/train \ # file, dir, webcam
+quite=True \ # Quite Output
yolo task.data.source={Any Source} # if pip installed
yolo task=inference task.data.source={Any}
```
### Validation
To validate model performance, or generate a json file in COCO format:
```shell
python yolo/lazy.py task=validation
python yolo/lazy.py task=validation dataset=toy
```
## Contributing
Contributions to the YOLO project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for guidelines on how to contribute.
## Star History
[](https://star-history.com/#MultimediaTechLab/YOLO&Date)
## Citations
```
@inproceedings{wang2022yolov7,
title={{YOLOv7}: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
year={2023},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}
@inproceedings{wang2024yolov9,
title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark},
year={2024},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
}
@inproceedings{tsui2024yolord,
author={Tsui, Hao-Tang and Wang, Chien-Yao and Liao, Hong-Yuan Mark},
title={{YOLO-RD}: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary},
booktitle={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2025},
}
```