Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (FCNs).

The implementation is largely based on the reference code provided by the authors of the paper link. The model was applied on the Scene Parsing Challenge dataset provided by MIT http://sceneparsing.csail.mit.edu/.

Prerequisites Results Observations Useful links Prerequisites

The results were obtained after training for ~6-7 hrs on a 12GB TitanX. The code was originally written and tested with tensorflow0.11 and python2.7. The tf.summary calls have been updated to work with tensorflow version 0.12. To work with older versions of tensorflow use branch tf.0.11_compatible. Some of the problems while working with tensorflow1.0 and in windows have been discussed in Issue #9. To train model simply execute python FCN.py To visualize results for a random batch of images use flag --mode=visualize debug flag can be set during training to add information regarding activations, gradients, variables etc. The IPython notebook in logs folder can be used to view results in color as below. Results

Results were obtained by training the model in batches of 2 with resized image of 256x256. Note that although the training is done at this image size - Nothing prevents the model from working on arbitrary sized images. No post processing was done on the predicted images. Training was done for 9 epochs - The shorter training time explains why certain concepts seem semantically understood by the model while others were not. Results below are from randomly chosen images from validation dataset.

Pretty much used the same network design as in the reference model implementation of the paper in caffe. The weights for the new layers added were initialized with small values, and the learning was done using Adam Optimizer (Learning rate = 1e-4).