<div align="center">
<h3>
Yolox-Pip: This is a packaged version of the YOLOX for easy installation and use.
</h3>
<h4>
<img width="800" alt="teaser" src="doc/fig.png">
</h4>
</div>
## <div align="center">Overview</div>
This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use.
### Installation
```
pip install yoloxdetect
```
### Yolox Inference
```python
from yoloxdetect import YoloxDetector
model = YoloxDetector(
model_path = "kadirnar/yolox_s-v0.1.1", # or "data/weights/yolox_s.pth"
config_path = "configs.yolox_s",
device = "cuda:0",
hf_model=True,
)
model.classes = None
model.conf = 0.25
model.iou = 0.45
model.show = False
model.save = True
pred = model.predict(image='data/images', img_size=640)
```
### Citation
```bibtex
@article{yolox2021,
title={YOLOX: Exceeding YOLO Series in 2021},
author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2107.08430},
year={2021}
}
```
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