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Developer Mode Build & Test Deploy Mode Validation & Inference
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Welcome to the official implementation of YOLOv71 and YOLOv92 , YOLO-RD3 . This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.
- This is the official YOLO model implementation with an MIT License.
- For quick deployment: you can directly install by pip+git:
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- 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
- YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary
To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:
git clone git@github.com:WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements.txtThese are simple examples. For more customization details, please refer to Notebooks and lower-level modifications HOWTO .
To train YOLO on your machine/dataset:
- Modify the configuration file
yolo/config/dataset/**.yamlto point to your dataset. - Run the training script:
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
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}To use a model for object detection, use:
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 +quiet=True \ # Quiet Output yolo task.data.source={Any Source} # if pip installed yolo task=inference task.data.source={Any}
To validate model performance, or generate a json file in COCO format:
python yolo/lazy.py task=validation python yolo/lazy.py task=validation dataset=toy
Contributions to the YOLO project are welcome! See CONTRIBUTING for guidelines on how to contribute.
@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},
}