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pspnet-pytorch

PyTorch implementation of PSPNet segmentation network

Original paper

Pyramid Scene Parsing Network

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