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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_s-v0.1.1", |
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config_path = "configs.yolox_s", |
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device = "cuda:0", |
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hf_model=True |
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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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``` |
