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.
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 to look for all available YOLOv3 models.
How to use
Installation
Follow Ryzen AI Installation to prepare the environment for Ryzen AI. Run the following script to install pre-requisites for this model.
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
- Run general_json2yolo.py to generate the labels folder and val2017.txt
python general_json2yolo.py
Finally, COCO dataset should look like this:
+ 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
on how to use
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
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)
- Test accuracy of the quantized model
python onnx_test.py --ipu --provider_config Path\To\vaip_config.json
Performance
Metric | Accuracy on IPU |
---|---|
[email protected]:0.95 | 0.389 |
@misc{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Joseph Redmon and Ali Farhadi},
year={2018},
eprint={1804.02767},
archivePrefix={arXiv},
primaryClass={cs.CV}
}