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

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

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}

python yolo/lazy.py task=validation
python yolo/lazy.py task=validation dataset=toy

@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},
}