readme: ---
  license: apache-2.0
  tags:
    - object-detection
    - computer-vision
    - yolox
    - yolov3
    - yolov5
  datasets:
    - detection-datasets/coco
  ---
  
  ### Model Description
  [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported.
  
  [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use.
  
  [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
  
  ### Installation
  ```
  pip install yoloxdetect
  ```
  
  ### Yolox Inference
  ```python
  from yoloxdetect import YoloxDetector
  from yolox.data.datasets import COCO_CLASSES
  model = YoloxDetector(
      model_path = "kadirnar/yolox_m-v0.1.1",
      config_path = "configs.yolox_m",
      device = "cuda:0",
      hf_model=True
  )
  model.classes = COCO_CLASSES
  model.conf = 0.25
  model.iou = 0.45
  model.show = False
  model.save = True
  pred = model.predict(image='data/images', img_size=640)
  ```
  
  ### BibTeX Entry and Citation Info
   ```
   @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}
  }
  ```