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---
license: apache-2.0
datasets:
- COCO
metrics:
- mAP
language:
- en
tags:
- RyzenAI
- object-detection
- vision
- YOLO
- Pytorch
---
# YOLOv3 model trained on COCO
YOLOv3 is trained on COCO object detection (118k annotated images) at resolution 416x416. It was released in https://github.com/ultralytics/yolov3/tree/v8.
We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com).
## Model description
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.
## Intended uses & limitations
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.
## 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.
1. Download COCO dataset
2. Run general_json2yolo.py to generate the labels folder and val2017.txt
```sh
python general_json2yolo.py
```
Finally, COCO dataset should look like this:
```plain
+ coco/
+ annotations/
+ instance_val2017.json
+ ...
+ images/
+ val2017/
+ 000000000139.jpg
+ 000000000285.jpg
+ ...
+ labels/
+ val2017/
+ 000000000139.txt
+ 000000000285.txt
+ ...
+ val2017.txt
```
### Test & Evaluation
- Code snippet from [`onnx_inference.py`](onnx_inference.py) on how to use
```python
onnx_path = "yolov3-8.onnx"
onnx_model = onnxruntime.InferenceSession(
onnx_path, providers=providers, provider_options=provider_options)
path = opt.img
new_path = os.path.join(opt.out, "demo_infer.jpg")
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,
multi_label=False,
classes=classes,
agnostic=agnostic_nms)
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
det = pred[0]
im0 = img0.copy()
if len(det):
# Rescale boxes from imgsz to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
# Stream results
cv2.imwrite(new_path, im0)
```
- Run inference for a single image
```sh
python onnx_inference.py --img INPUT_IMG_PATH --out OUTPUT_DIR --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 onnx_test.py --ipu --provider_config Path\To\vaip_config.json
```
### Performance
|Metric |Accuracy on IPU|
| :----: | :----: |
|AP\@0.50:0.95|0.389|
```bibtex
@misc{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Joseph Redmon and Ali Farhadi},
year={2018},
eprint={1804.02767},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |