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--- |
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license: apache-2.0 |
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tags: |
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- RyzenAI |
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- object-detection |
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- vision |
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- YOLO |
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- Pytorch |
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datasets: |
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- COCO |
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metrics: |
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- mAP |
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--- |
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# YOLOv5s model trained on COCO |
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YOLOv5s is the small version of YOLOv5 model trained on COCO object detection (118k annotated images) at resolution 640x640. It was released in [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5). |
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We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html). |
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## Model description |
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YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. |
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## Intended uses & limitations |
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You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=amd/yolov5) to look for all available YOLOv5 models. |
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## How to use |
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### Installation |
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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Run the following script to install pre-requisites for this model. |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Data Preparation (optional: for accuracy evaluation) |
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The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation. |
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Download COCO dataset and create directories in your code like this: |
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```plain |
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└── datasets |
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└── coco |
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├── annotations |
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| ├── instances_val2017.json |
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| └── ... |
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├── labels |
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| ├── val2017 |
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| | ├── 000000000139.txt |
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| ├── 000000000285.txt |
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| └── ... |
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├── images |
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| ├── val2017 |
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| | ├── 000000000139.jpg |
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| ├── 000000000285.jpg |
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└── val2017.txt |
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``` |
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1. put the val2017 image folder under images directory or use a softlink |
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2. the labels folder and val2017.txt above are generate by **general_json2yolo.py** |
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3. modify the coco.yaml like this: |
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```markdown |
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path: /path/to/your/datasets/coco # dataset root dir |
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train: train2017.txt # train images (relative to 'path') 118287 images |
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val: val2017.txt # val images (relative to 'path') 5000 images |
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``` |
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### Test & Evaluation |
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- Code snippet from [`onnx_inference.py`](onnx_inference.py) on how to use |
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```python |
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args = make_parser().parse_args() |
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onnx_path = args.model |
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onnx_model = onnxruntime.InferenceSession(onnx_path) |
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grid = np.load("./grid.npy", allow_pickle=True) |
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anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True) |
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path = args.image_path |
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new_path = args.output_path |
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conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000 |
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img0 = cv2.imread(path) |
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img = pre_process(img0) |
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onnx_input = {onnx_model.get_inputs()[0].name: img} |
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onnx_output = onnx_model.run(None, onnx_input) |
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onnx_output = post_process(onnx_output) |
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pred = non_max_suppression( |
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onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det |
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) |
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colors = Colors() |
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det = pred[0] |
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im0 = img0.copy() |
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annotator = Annotator(im0, line_width=2, example=str(names)) |
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if len(det): |
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# Rescale boxes from img_size to im0 size |
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |
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# Write results |
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for *xyxy, conf, cls in reversed(det): |
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c = int(cls) # integer class |
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label = f"{names[c]} {conf:.2f}" |
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annotator.box_label(xyxy, label, color=colors(c, True)) |
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# Stream results |
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im0 = annotator.result() |
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cv2.imwrite(new_path, im0) |
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``` |
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- Run inference for a single image |
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```python |
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python onnx_inference.py -m ./yolov5s_qat.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config |
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``` |
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*Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))* |
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- Test accuracy of the quantized model |
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```python |
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python onnx_eval.py -m ./yolov5s_qat.onnx --ipu --provider_config /Path/To/Your/Provider_config |
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``` |
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### Performance |
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|Metric |Accuracy on IPU| |
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| :----: | :----: | |
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|AP\@0.50:0.95|0.356| |
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```bibtex |
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@software{glenn_jocher_2021_5563715, |
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author = {Glenn Jocher et. al.}, |
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title = {{ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, |
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Roboflow integration, TensorFlow export, OpenCV |
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DNN support}}, |
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month = oct, |
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year = 2021, |
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publisher = {Zenodo}, |
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version = {v6.0}, |
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doi = {10.5281/zenodo.5563715}, |
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url = {https://doi.org/10.5281/zenodo.5563715} |
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} |
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``` |
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