pspnet-pytorch
PyTorch implementation of PSPNet segmentation network
Original paper
Details
This is a slightly different version - instead of direct 8x upsampling at the end I use three consequitive upsamplings for stability.
Feature extraction
Using pretrained weights for extractors - improved quality and convergence dramatically.
Currently supported:
- SqueezeNet
- DenseNet-121
- ResNet-18
- ResNet-34
- ResNet-50
- ResNet-101
- ResNet-152
Planned:
- DenseNet-169
- DenseNet-201
Usage
To follow the training routine in train.py you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
