Image Data Augmentation

Sample Redistribution

Introduced by Guo et al. in Sample and Computation Redistribution for Efficient Face Detection

Sample Redistribution is a data augmentation technique for face detection which augments training samples based on the statistics of benchmark datasets via large-scale cropping. During training data augmentation, square patches are cropped from the original images with a random size from the set $[0.3,1.0]$ of the short edge of the original images. To generate more positive samples for stride 8, the random size range is enlarged from $[0.3,1.0]$ to $[0.3,2.0]$. When the crop box is beyond the original image, average RGB values fill the missing pixels.

The motivation is that for efficient face detection under a fixed VGA resolution (i.e. 640×480), most of the faces (78.93%) in WIDER FACE are smaller than 32×32 pixels, and thus they are predicted by shallow stages. To obtain more training samples for these shallow stages, Sample Redistribution (SR) is used.

Source: Sample and Computation Redistribution for Efficient Face Detection


Paper Code Results Date Stars


Task Papers Share
Face Detection 1 100.00%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign