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.