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--- |
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license: apache-2.0 |
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inference: false |
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tags: |
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- object-detection |
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- computer-vision |
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- vision |
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- mmdet |
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- sahi |
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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: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430) |
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[SAHI: Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://arxiv.org/abs/2202.06934) |
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Improved anchor-free YOLO architecture for object detection task. |
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### Documents |
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- [GitHub Repo](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/README.md) |
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- [Paper - YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430) |
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### Datasets |
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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. |
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### How to use |
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- Install [sahi](https://github.com/obss/sahi) and `mmdet`: |
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```bash |
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pip install -U sahi |
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pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html |
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pip install mmdet==2.26.0 |
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``` |
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- Load model and perform prediction: |
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```python |
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from sahi import AutoDetectionModel |
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from sahi.utils.file import download_from_url |
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from sahi.predict import get_prediction |
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from sahi.cv import read_image_as_pil |
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MMDET_YOLOX_TINY_MODEL_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/resolve/main/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth" |
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MMDET_YOLOX_TINY_MODEL_PATH = "yolox.pt" |
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MMDET_YOLOX_TINY_CONFIG_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/raw/main/yolox_tiny_8x8_300e_coco.py" |
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MMDET_YOLOX_TINY_CONFIG_PATH = "config.py" |
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IMAGE_URL = "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg" |
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# download weight and config |
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download_from_url( |
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MMDET_YOLOX_TINY_MODEL_URL, |
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MMDET_YOLOX_TINY_MODEL_PATH, |
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) |
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download_from_url( |
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MMDET_YOLOX_TINY_CONFIG_URL, |
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MMDET_YOLOX_TINY_CONFIG_PATH, |
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) |
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# create model |
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detection_model = AutoDetectionModel.from_pretrained( |
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model_type='mmdet', |
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model_path=MMDET_YOLOX_TINY_MODEL_PATH, |
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config_path=MMDET_YOLOX_TINY_CONFIG_PATH, |
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confidence_threshold=0.5, |
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device="cuda:0", # or 'cpu' |
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) |
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# prepare input image |
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image = read_image_as_pil(IMAGE_URL) |
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# perform prediction |
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prediction_result = get_prediction( |
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image=image, |
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detection_model=detection_model |
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) |
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# visualize predictions |
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prediction_result.export_predictions(export_dir='results/') |
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# get predictions |
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prediction_result.object_prediction_list |
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``` |
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More info at [demo notebook](https://github.com/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb). |
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### BibTeX Entry and Citation Info |
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``` |
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@article{akyon2022sahi, |
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title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, |
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author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, |
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journal={2022 IEEE International Conference on Image Processing (ICIP)}, |
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doi={10.1109/ICIP46576.2022.9897990}, |
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pages={966-970}, |
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year={2022} |
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} |
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``` |
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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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``` |
