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--- |
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license: apache-2.0 |
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datasets: |
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- COCO |
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metrics: |
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- mAP |
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language: |
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- en |
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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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--- |
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# YOLOv3 model trained on COCO |
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YOLOv3 is trained on COCO object detection (118k annotated images) at resolution 416x416. It was released in https://github.com/ultralytics/yolov3/tree/v8. |
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We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com). |
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## Model description |
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YOLOv3 🚀 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/yolov3) to look for all available YOLOv3 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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1. Download COCO dataset |
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2. Run general_json2yolo.py to generate the labels folder and val2017.txt |
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```sh |
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python general_json2yolo.py |
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``` |
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Finally, COCO dataset should look like this: |
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```plain |
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+ coco/ |
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+ annotations/ |
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+ instance_val2017.json |
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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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+ ... |
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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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+ val2017.txt |
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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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onnx_path = "yolov3-8.onnx" |
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onnx_model = onnxruntime.InferenceSession( |
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onnx_path, providers=providers, provider_options=provider_options) |
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path = opt.img |
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new_path = os.path.join(opt.out, "demo_infer.jpg") |
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conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, \ |
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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], |
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conf_thres, |
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iou_thres, |
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multi_label=False, |
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classes=classes, |
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agnostic=agnostic_nms) |
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colors = [[random.randint(0, 255) for _ in range(3)] |
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for _ in range(len(names))] |
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det = pred[0] |
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im0 = img0.copy() |
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if len(det): |
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# Rescale boxes from imgsz 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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label = '%s %.2f' % (names[int(cls)], conf) |
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) |
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# Stream results |
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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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```sh |
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python onnx_inference.py --img INPUT_IMG_PATH --out OUTPUT_DIR --ipu --provider_config Path\To\vaip_config.json |
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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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```sh |
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python onnx_test.py --ipu --provider_config Path\To\vaip_config.json |
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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|0.389| |
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```bibtex |
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@misc{redmon2018yolov3, |
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title={YOLOv3: An Incremental Improvement}, |
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author={Joseph Redmon and Ali Farhadi}, |
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year={2018}, |
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eprint={1804.02767}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
