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---
license: apache-2.0
tags:
- RyzenAI
- object-detection
- vision
- YOLO
- anchor-free
- pytorch
datasets:
- coco
metrics:
- mAP
---
# YOLOX-small model trained on COCO
YOLOX-small is the small version of YOLOX model trained on COCO object detection (118k annotated images) at resolution 640x640. It was introduced in the paper [YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430) by Zheng Ge et al. and first released in [this repository](https://github.com/Megvii-BaseDetection/YOLOX).
We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com).
## Model description
Based on YOLO detector, the YOLOX model adopts anchor-free head and conducts other advanced detection techniques including decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models. The series of models were developed by Megvii Inc. and won the 1st Place on Streaming Perception Challenge (WAD at CVPR 2021).
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=amd/yolox) to look for all available YOLOX models.
## How to use
### Installation
Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
Run the following script to install pre-requisites for this model.
```sh
pip install -r requirements.txt
```
### Data Preparation (optional: for accuracy evaluation)
The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation.
Download the validation set of COCO dataset ([val2017.zip](http://images.cocodataset.org/zips/val2017.zip) and [annotations_trainval2017.zip](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)).
Then unzip the files and move them to the following directories (or create soft links):
```plain
└── data
└── COCO
├── annotations
| ├── instances_val2017.json
| └── ...
└── val2017
├── 000000000139.jpg
├── 000000000285.jpg
└── ...
```
### Test & Evaluation
- Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
```python
args = make_parser().parse_args()
input_shape = tuple(map(int, args.input_shape.split(',')))
origin_img = cv2.imread(args.image_path)
img, ratio = preprocess(origin_img, input_shape)
if args.ipu:
providers = ["VitisAIExecutionProvider"]
provider_options = [{"config_file": args.provider_config}]
else:
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
provider_options = None
session = ort.InferenceSession(args.model, providers=providers, provider_options=provider_options)
# NCHW format
# ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
# NHWC format
ort_inputs = {session.get_inputs()[0].name: np.transpose(img[None, :, :, :], (0, 2 ,3, 1))}
outputs = session.run(None, ort_inputs)
outputs = [np.transpose(out, (0, 3, 1, 2)) for out in outputs] # for NHWC format
dets = postprocess(outputs, input_shape, ratio)
if dets is not None:
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
origin_img = vis(origin_img, final_boxes, final_scores, final_cls_inds,
conf=args.score_thr, class_names=COCO_CLASSES)
mkdir(args.output_dir)
output_path = os.path.join(args.output_dir, os.path.basename(args.image_path))
cv2.imwrite(output_path, origin_img)
```
- Run inference for a single image
```sh
python infer_onnx.py -m yolox-s-int8.onnx -i Path\To\Your\Image --ipu --provider_config Path\To\vaip_config.json
```
*Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))*
- Test accuracy of the quantized model
```sh
python eval_onnx.py -m yolox-s-int8.onnx --ipu --provider_config Path\To\vaip_config.json
```
### Performance
|Metric | Accuracy on IPU|
| :----: | :----: |
|AP\@0.50:0.95|0.370|
```bibtex
@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}
}
```
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