💎1MB lightweight face detection model (1MB轻量级人脸检测模型)
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This model is a lightweight facedetection model designed for edge computing devices.
The training set is the VOC format data set generated by using the cleaned widerface labels provided by Retinaface in conjunction with the widerface data set (PS: the following test results were obtained by myself, and the results may be partially inconsistent).
|Easy Set||Medium Set||Hard Set|
|Easy Set||Medium Set||Hard Set|
- This part mainly tests the effect of the test set under the medium and small resolutions.
- RetinaFace-mnet (Retinaface-Mobilenet-0.25), from a great job insightface, when testing this network, the original image is scaled by 320 or 640 as the maximum side length, so the face will not be deformed, and the rest of the networks will have a fixed size resize. At the same time, the result of the RetinaFace-mnet optimal 1600 single-scale val set was 0.887 (Easy) / 0.87 (Medium) / 0.791 (Hard).
|1 core||2 core||3 core||4 core|
|Official Retinaface-Mobilenet-0.25 (Mxnet)||46||25||18.5||15|
|model file size（MB）|
|Official Retinaface-Mobilenet-0.25 (Mxnet)||1.68|
(PS: If you download the filtered packets in (1) above, you don't need to perform this step) Because the wideface has many small and unclear faces, which is not conducive to the convergence of efficient models, it needs to be filtered for training.By default,faces smaller than 10 pixels by 10 pixels will be filtered. run ./data/widerface2vocaddlandmark.py ```Python python3 ./data/widerface2vocaddlandmark.py
After the program is run and finished, the **wider_face_add_lm_10_10** folder will be generated in the ./data directory. The folder data and data package (1) are the same after decompression. The complete directory structure is as follows:Shell data/ retinafacelabels/ test/ train/ val/ widerface/ WIDERtest/ WIDERtrain/ WIDERval/ widerfaceaddlm1010/ Annotations/ ImageSets/ JPEGImages/ widerface2vocadd_landmark.py ```
At this point, the VOC training set is ready. There are two scripts: train-version-slim.sh and train-version-RFB.sh in the root directory of the project. The former is used to train the slim version model, and the latter is used. Training RFB version model, the default parameters have been set, if the parameters need to be changed, please refer to the description of each training parameter in ./train.py.
Run train-version-slim.sh train-version-RFB.sh
Shell sh train-version-slim.sh or sh train-version-RFB.sh
(1) Optimal: input size input_size: 640 (640x480) resolution training, and use the same or larger input size for inference, such as using the provided pre-training model version-slim-640.pth or version-RFB-640.pth for inference, lower False positives.
(2) Sub-optimal: input size input_size: 320 (320x240) resolution training, and use 480x360 or 640x480 size input for predictive reasoning, more sensitive to small faces, false positives will increase. - The best results for each scene require adjustment of the input resolution to strike a balance between speed and accuracy. - Excessive input resolution will enhance the recall rate of small faces, but it will also increase the false positive rate of large and close-range faces, and the speed of inference will increase exponentially. - Too small input resolution will significantly speed up the inference, but it will greatly reduce the recall rate of small faces. - The input resolution of the production scene should be as consistent as possible with the input resolution of the model training, and the up and down floating should not be too large.