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README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- object-detection
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- computer-vision
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- yolox
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- yolov3
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- yolov5
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datasets:
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- detection-datasets/coco
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---
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### Model Description
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[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.
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[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.
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[Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
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### Installation
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```
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pip install yoloxdetect
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```
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### Yolox Inference
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```python
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from yoloxdetect import YoloxDetector
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from yolox.data.datasets import COCO_CLASSES
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model = YoloxDetector(
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model_path = "kadirnar/yolox_tiny-v0.1.1",
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config_path = "configs.yolox_tiny",
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device = "cuda:0",
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)
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model.classes = COCO_CLASSES
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model.conf = 0.25
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model.iou = 0.45
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model.show = False
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model.save = True
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pred = model.predict(image='data/images', img_size=640)
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```
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### BibTeX Entry and Citation Info
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```
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@article{yolox2021,
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title={YOLOX: Exceeding YOLO Series in 2021},
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author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
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journal={arXiv preprint arXiv:2107.08430},
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year={2021}
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}
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```
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