ixrt

RetinaFace (ixRT)

Model Description

RetinaFace is an efficient single-stage face detection model that employs a multi-task learning strategy to simultaneously predict facial locations, landmarks, and 3D facial shapes. It utilizes feature pyramids and context modules to extract multi-scale features and employs a self-supervised mesh decoder to enhance detection accuracy. RetinaFace demonstrates excellent performance on datasets like WIDER FACE, supports real-time processing, and its code and datasets are publicly available for researchers.

Supported Environments

GPU	IXUCA SDK	Release	Branch
MR-V100	4.4.0	26.03	release/26.03
MR-V100	4.3.0	25.12	release/25.12

Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。

切换分支命令示例：git checkout release/26.03

Model Preparation

Prepare Resources

Pretrained model: https://github.com/biubug6/Face-Detector-1MB-with-landmark/raw/master/weights/mobilenet0.25_Final.pth

Dataset: http://shuoyang1213.me/WIDERFACE/ to download the validation dataset.

wget https://github.com/biubug6/Face-Detector-1MB-with-landmark/raw/master/weights/mobilenet0.25_Final.pth

Install Dependencies

# Install libGL
## CentOS
yum install -y mesa-libGL
## Ubuntu
apt install -y libgl1-mesa-glx

pip3 install -r requirements.txt

python3 setup.py build_ext --inplace

Model Conversion

# export onnx model
python3 torch2onnx.py --model mobilenet0.25_Final.pth --onnx_model mnetv1_retinaface.onnx

Model Inference

export DATASETS_DIR=/Path/to/widerface/
export GT_DIR=../igie/widerface_evaluate/ground_truth

FP16

# Accuracy
bash scripts/infer_retinaface_fp16_accuracy.sh
# Performance
bash scripts/infer_retinaface_fp16_performance.sh

Model Results

Model	BatchSize	Precision	FPS	Easy AP(%)	Medium AP (%)	Hard AP(%)
RetinaFace	32	FP16	8536.367	80.84	69.34	37.31

References

• Face-Detector-1MB-with-landmark