SCRFD issues

Hi! I’m testing your new SCRFD face detector and have noticed some issues with onnx inference code and network outputs:

1. In scrfd.py line 275 you are filtering bboxes, but later at line 278 you return det, so max_num parameter have no effect and may cause exceptions.

2. Later at line 335 you are calling detector without providing input shape, which wont work with model having dynamic shape. However it won’t be an issue when called from face_analysis.py

3. I have noticed that detector returns very low scores or even fails on faces occupying >40% of image, it’s especially visible for square shaped images, when there can’t be provided additional padding during resize process. Also I have noticed that in such cases accuracy increases when lowering detection size (i.e. 480x480), and decreases when increasing it (i.e 1024x1024). Here is an example of detection at 640x640 scale: Original image size is 1200x1200. As you can see when detection is run with resize to 640x640 score is 0.38 For 480x480 score is 0.86, and for 736x736 score is 0.07. Same behavior is noticed for both scrfd_10g_bnkps and scrfd_2.5g_bnkps models. In some cases it might be fixed by adding fixed padding around image, but it might lead to decreased accuracy for other image types, so it can’t be applied by default.

BTW: Thanks for your great work!

[SthPhoenix]

@nttstar , I have retrained scrfd_2.5g_bnkps, with batch norm replaced with group norm (new model should be called scrfd_2.5g_gnkps i think), just like for scrfd_2.5g model, I achieved following WiderFace AP:

Model	Easy	Medium	Hard
scrfd_2.5g_bnkps	93.80	92.02	77.13
scrfd_2.5g_gnkps	93.57	91.70	76.08

As you can see this config gives a small accuracy decrease, while completely solves problem with large faces. For above example I’m getting score around 0.7

[SthPhoenix]

Then i change to scrfd_34g_gnkps, it works, but GN not supported by tensorRT(@SthPhoenix can you tell me how to transform GN onnx models to RT models?)

In TensorRT you should load optional layers support, for Python TRT API you should just add this line just after import:

import tensorrt as trt
trt.init_libnvinfer_plugins(None, "")