grseb9s / matconvnet-fcn

A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation

MatConvNet implementation of the FCN models for semantic segmentation

This package contains an implementation of the FCN models (training and evaluation) using the MatConvNet library.

For training, look at the fcnTrain.m script, and for evaluation at fcnTest.m. The script fcnTestModelZoo.m is designed to test third party networks imported in MatConvNet (mainly from Caffe).

While we are still tuning parameters, on the PASCAL VOC 2011 validation data subset used in the FCN paper, this code has been used to train networks with this performance:

Model	Test data	Mean IOU	Mean pix. accuracy	Pixel accuracy
FCN-32s (ours)	RV-VOC11	60.80	89.61	75.49
FCN-16s (ours)	RV-VOC11	62.25	90.08	77.81
FCN-8s (ours)	RV-VOC11	in prog.	in prog.	in prog.
FNC-32s (orig.)	RV-VOC11	59.43	89.12	73.28
FNC-16s (orig.)	RV-VOC11	62.35	90.02	75.74
FNC-8s (orig.)	RV-VOC11	62.69	90.33	75.86

The original FCN models can be downloaded from the MatConvNet model repository.

About

This code was developed by

• Sebastien Ehrhardt
• Andrea Vedaldi

References

'Fully Convolutional Models for Semantic Segmentation', Jonathan Long, Evan Shelhamer and Trevor Darrell, CVPR, 2015 (paper).

Changes

• v0.9.1 -- Bugfixes.
• v0.9 -- Initial release. FCN32s and FCN16s work well.