Mask-RCNN
A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch
Decription of folders
- model.py includes the models of ResNet and FPN which were already implemented by the authors of the papers and reproduced in this implementation
- nms and RoiAlign are taken from Robb Girshick's implementation of faster RCNN
- Focal loss has been added to this implementtaion on lieu of better results as evidenced by the paper on RetinaNets
Mask-RCNN model:
Features:
- The part of the network responsible for bounding box detection derives it's inspiration from the faster RCNN model having a RPN working in tandem with a ConvNet
- The pooling layers present in the ConvNet round down or round up to the nearest integer when the stride is not a divisor of the
receptive field, which tends to either lose or assume "information" from the image respectively at the non integral points. - ROI align was proposed to deal with this, wherein bilinear interpolation is used to detect the values at the non integral values of the pixels
- Using a more complex interpolation scheme( cubic interpolation -> 16 additional features) offers a slightly better result when this model was tested, however not enough to justify the additional complexity
- Cross entropy loss when summed over a huge number of proposals tends to take a huge value for proposals that have a high confidence metric thereby dwarfing the contribution from the proposals of interest. Focal Loss was proposed to do away with this problem
- However Focal loss gives much better results with single stage networks. This is because a two stage network has some discriminative policy to deal with this class imbalance something which the single stage networks don't enjoy.
