add model card

README.md

@@ -1,3 +1,105 @@
 ---
 license: apache-2.0
+
+tags:
+- object-detection
+- computer-vision
+
+datasets:
+- coco
 ---
+
+### Model Description
+[YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)
+
+Improved anchor-free YOLO architecture for object detection task.
+
+
+### Documents
+- [GitHub Repo](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/README.md)
+- [Paper - YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)
+  
+### Datasets
+The YOLOX model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. 
+
+
+### How to use
+
+- Install [sahi](https://github.com/obss/sahi) and `mmdet`:
+
+```bash
+pip install -U sahi
+pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
+pip install mmdet==2.26.0
+```
+
+- Load model and perform prediction:
+
+```python
+from sahi import AutoDetectionModel
+from sahi.utils.file import download_from_url
+from sahi.predict import get_prediction
+from sahi.cv import read_image_as_pil
+
+MMDET_YOLOX_TINY_MODEL_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/resolve/main/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth"
+MMDET_YOLOX_TINY_MODEL_PATH = "yolox.pt"
+MMDET_YOLOX_TINY_CONFIG_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/raw/main/yolox_tiny_8x8_300e_coco.py"
+MMDET_YOLOX_TINY_CONFIG_PATH = "config.py"
+IMAGE_URL = "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg"
+
+# download weight and config
+download_from_url(
+  MMDET_YOLOX_TINY_MODEL_URL,
+  MMDET_YOLOX_TINY_MODEL_PATH,
+)
+download_from_url(
+  MMDET_YOLOX_TINY_CONFIG_URL,
+  MMDET_YOLOX_TINY_CONFIG_PATH,
+)
+
+# create model
+detection_model = AutoDetectionModel.from_pretrained(
+    model_type='mmdet',
+    model_path=MMDET_YOLOX_TINY_MODEL_PATH,
+    config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
+    confidence_threshold=0.5,
+    device="cuda:0", # or 'cpu'
+)
+
+# prepare input image
+image = read_image_as_pil(IMAGE_URL)
+
+# perform prediction
+prediction_result = get_prediction(
+  image=image,
+  detection_model=detection_model
+)
+
+# visualize predictions
+prediction_result.export_predictions(export_dir='results/')
+
+# get predictions
+prediction_result.object_prediction_list
+```
+
+More info at [demo notebook](https://github.com/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb).
+
+### BibTeX Entry and Citation Info
+```
+@article{akyon2022sahi,
+  title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
+  author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
+  journal={2022 IEEE International Conference on Image Processing (ICIP)},
+  doi={10.1109/ICIP46576.2022.9897990},
+  pages={966-970},
+  year={2022}
+}
+```
+```
+@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}
+}
+```