laiethanlai / yolov9mit

An MIT rewrite of YOLOv9

YOLO: Official Implementation of YOLOv{7, 9}

Important

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.

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.

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.

TL;DR

• This is the official YOLO model implementation with an MIT License.
• For quick deployment: you can enter directly in the terminal:

pip install [email protected]:WongKinYiu/YOLO.git
yolo task=inference task.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
• YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

Installation

To get started with YOLOv9, clone this repository and install the required dependencies:

git clone [email protected]:WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements.txt

Features

• Autodownload weights/datasets
• Pip installable
• Support for devices:
  • CUDA
  • MPS (PyTorch 2.3+)
  • CPU
• Task:
  • Training
  • Inference
  • Validation

Task

These are simple examples. For more customization details, please refer to Notebooks and lower-level modifications HOWTO.

Training

To train YOLOv9 on your dataset:

1. Modify the configuration file data/config.yaml to point to your dataset.
2. Run the training script:

python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c

Transfer Learning

To perform transfer learning with YOLOv9:

python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda}

Inference

To evaluate the model performance, use:

python yolo/lazy.py task=inference weight=weights/v9-c-deploy.pt model=v9-c-deploy # use deploy weight
python python yolo/lazy.py task=inference # if cloned from GitHub
yolo task=inference task.data.source={Any} # if pip installed

Validation [WIP]

To validate the model performance, use:

# Work In Progress...

Contributing

Contributions to the YOLOv9 project are welcome! See CONTRIBUTING for guidelines on how to contribute.

Star History

Citations

@misc{wang2024yolov9,
      title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
      author={Chien-Yao Wang and I-Hau Yeh and Hong-Yuan Mark Liao},
      year={2024},
      eprint={2402.13616},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}