Update README.md

README.md

@@ -1,3 +1,50 @@
 ---
 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_x-v0.1.1",
+    config_path = "configs.yolox_x",
+    device = "cuda:0",
+)
+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}
+}
+```