LFFD-A-Light-and-Fast-Face-Detector-for-Edge-Devices
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YonghaoHe
Detecting Tiny Faces
Hi, How should I change the architecture of network to make it robust to hard cases (small faces). In my scenario faces are mostly tiny. I reduced param_bbox_small_list and param_bbox_large_list and retrained the network but I'm getting no detections at all.
Could you please elaborate a bit more, for instance, what are the shapes of hard case faces in your dataset(just the bounding boxes), and what are the different param_bbox_list values you have tried?
Did you try changing the feature_map_size list and training it?
Input shape of your image and if possible share your configuration file so that I will try to help you.
Most of the faces in my dataset are no larger than (30,30)... the quality of the image itself is not good too so lot of hard cases.
I tried changing param_bbox_small_list = [10, 20, 40, 80, 160] to param_bbox_small_list = [10, 20, 30, 40, 50]
and the upper bound from param_bbox_large_list = [20, 40, 80, 160, 320] to param_bbox_large_list = [20, 30, 40, 50, 60]
input shape of my image is either 360x360 or 320x240 or 240x320. I didn't change any other thing.
and I don't want to train it on my dataset. I just want to train it on WIDERFACE in a way which can get more robust to tiny faces.
What I detect is the face in the picture of about 100*200,and most of the faces in my dataset are no larger than (30,30).Did you find a good solution in the end, or use other model training?
