| | --- |
| | license: apache-2.0 |
| | tags: |
| | - RyzenAI |
| | - object-detection |
| | - vision |
| | - YOLO |
| | - Pytorch |
| | datasets: |
| | - COCO |
| | metrics: |
| | - mAP |
| | --- |
| | # YOLOv5s model trained on COCO |
| |
|
| | 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). |
| |
|
| | We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html). |
| |
|
| |
|
| | ## Model description |
| |
|
| | 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. |
| |
|
| |
|
| | ## Intended uses & limitations |
| |
|
| | 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. |
| |
|
| |
|
| | ## 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. |
| | ```bash |
| | 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 COCO dataset and create directories in your code like this: |
| | ```plain |
| | └── datasets |
| | └── coco |
| | ├── annotations |
| | | ├── instances_val2017.json |
| | | └── ... |
| | ├── labels |
| | | ├── val2017 |
| | | | ├── 000000000139.txt |
| | | ├── 000000000285.txt |
| | | └── ... |
| | ├── images |
| | | ├── val2017 |
| | | | ├── 000000000139.jpg |
| | | ├── 000000000285.jpg |
| | └── val2017.txt |
| | ``` |
| | 1. put the val2017 image folder under images directory or use a softlink |
| | 2. the labels folder and val2017.txt above are generate by **general_json2yolo.py** |
| | 3. modify the coco.yaml like this: |
| | ```markdown |
| | path: /path/to/your/datasets/coco # dataset root dir |
| | train: train2017.txt # train images (relative to 'path') 118287 images |
| | val: val2017.txt # val images (relative to 'path') 5000 images |
| | ``` |
| | |
| | |
| | ### Test & Evaluation |
| | |
| | - Code snippet from [`onnx_inference.py`](onnx_inference.py) on how to use |
| | ```python |
| | args = make_parser().parse_args() |
| | onnx_path = args.model |
| | onnx_model = onnxruntime.InferenceSession(onnx_path) |
| | grid = np.load("./grid.npy", allow_pickle=True) |
| | anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True) |
| | path = args.image_path |
| | new_path = args.output_path |
| | conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000 |
| | |
| | img0 = cv2.imread(path) |
| | img = pre_process(img0) |
| | onnx_input = {onnx_model.get_inputs()[0].name: img} |
| | onnx_output = onnx_model.run(None, onnx_input) |
| | onnx_output = post_process(onnx_output) |
| | pred = non_max_suppression( |
| | onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det |
| | ) |
| | colors = Colors() |
| | det = pred[0] |
| | im0 = img0.copy() |
| | annotator = Annotator(im0, line_width=2, example=str(names)) |
| | if len(det): |
| | # Rescale boxes from img_size to im0 size |
| | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |
| | |
| | # Write results |
| | for *xyxy, conf, cls in reversed(det): |
| | c = int(cls) # integer class |
| | label = f"{names[c]} {conf:.2f}" |
| | annotator.box_label(xyxy, label, color=colors(c, True)) |
| | # Stream results |
| | im0 = annotator.result() |
| | cv2.imwrite(new_path, im0) |
| | ``` |
| | |
| | - Run inference for a single image |
| | ```python |
| | python onnx_inference.py -m ./yolov5s_qat.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config |
| | ``` |
| | *Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))* |
| | - Test accuracy of the quantized model |
| | ```python |
| | python onnx_eval.py -m ./yolov5s_qat.onnx --ipu --provider_config /Path/To/Your/Provider_config |
| | ``` |
| | |
| | ### Performance |
| | |
| | |Metric |Accuracy on IPU| |
| | | :----: | :----: | |
| | |AP\@0.50:0.95|0.356| |
| | |
| | |
| | ```bibtex |
| | @software{glenn_jocher_2021_5563715, |
| | author = {Glenn Jocher et. al.}, |
| | title = {{ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, |
| | Roboflow integration, TensorFlow export, OpenCV |
| | DNN support}}, |
| | month = oct, |
| | year = 2021, |
| | publisher = {Zenodo}, |
| | version = {v6.0}, |
| | doi = {10.5281/zenodo.5563715}, |
| | url = {https://doi.org/10.5281/zenodo.5563715} |
| | } |
| | ``` |
| | |
