!under development!

Convolutional deep network framework for semantic segmentation, implemented using Theano.

Just do

    git clone https://github.com/iborko/theano-conv-semantic

Later, you can update your repo with

    git pull

This framework is heavily based on the work of Farabet-Pami. As in the aforementioned paper, training has 2 stages: in first stage only the feature selector is trained (convolutinal layers + last softmax layer), in second stage fully connected layer is added at the end of the network and only the newly added layer is trained.

Stanford background can be trained on two types of data: 1 scale YUV transformed images, 3 scales of laplacian pyramid YUV transformed images. Details are described in the aforementioned work.

1. Download MSRC-21, Stanford Background (iccv09) or KITTI dataset (German Ros version) and unpack them to the data/ folder.
2. Update dataset locations in generate-iccv.conf file and in generate-kitti.conf. Also, you can change other parameters like validation set percentage, output folder locations. generate-*.conf config files are used during the generation of theano-ready dataset.
3. Run python generate_msrc.py, python generate_iccv_1l.py generate-iccv.conf, python generate_iccv_3l.py generate-iccv.conf or python generate_kitti.py generate-kitti.conf.

• generate_msrc.py generates single-scale data for MSRC dataset
• generate_iccv_1l.py generates single-scale data for Stanford Background dataset
• generate_iccv_3l.py generates 3-scale data for Stanfrod Background dataset.
• generate_kitti.py generates 3-scale data from KITTI dataset.

4. Run python train_2step_3l.py file.conf or python train_kitti.py file.conf where file.conf is configuration file. Inside it you can set theano dataset location (produced by generate... script), network parameters, stopping parameters, etc.
5. To calculate results on test set you can run validate_iccv.py or validate_kitti.py script. Parameters are described at the bottom of the script. Example: python validate.py network.conf network-12-34.bin test where network-12-34.bin contains best network parameters (automatically generated during training).

Using RGB images + depth component calculated using stereo vision (spsstereo algorithm) and normalized

During runtime, framework generates output.log file. Data from it can be visualized using plot_cost.py script. Just run

    python plot_cost.py output.log

Script plots graphs of training cost, validation cost and validation error using pyplot.

• Usage of SIFT Flow dataset
• Framework will be able to load network architecture from configuration file (partially done)
• Input data generation will be configured through special configuration file (partially done)
• Support for oversegmentation methods (currently not supported) like superpixels (done)
• Implementation of the Inception layer described in GoogLeNet paper (partially done, helpers/layers/...)

Ivan Borko, Faculty of electrical engineering and computing, University of Zagreb, Croatia

2014/2015