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Keras implementation of DilatedNet for semantic segmentation

<div style="text-align: center" /> <img src="http://nicolovaligi.com/cat.jpg" style="max-width: 500px" /> </div>

A native Keras implementation of semantic segmentation according to Multi-Scale Context Aggregation by Dilated Convolutions (2016). Optionally uses the pretrained weights by the authors'.

The code has been tested on Tensorflow 1.3, Keras 1.2, and Python 3.6.

Using the pretrained model

Download and extract the pretrained model:

curl -L https://github.com/nicolov/segmentation_keras/releases/download/model/nicolov_segmentation_model.tar.gz | tar xvf -

Install dependencies and run:

pip install -r requirements.txt
# For GPU support
pip install tensorflow-gpu==1.3.0

python predict.py --weights_path conversion/converted/dilation8_pascal_voc.npy

The output image will be under images/cat_seg.png.

Converting the original Caffe model

Follow the instructions in the conversion folder to convert the weights to the TensorFlow format that can be used by Keras.

Training

Download the Augmented Pascal VOC dataset here:

curl -L http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz | tar -xvf -

This will create a benchmark_RELEASE directory in the root of the repo. Use the convert_masks.py script to convert the provided masks in .mat format to RGB pngs:

python convert_masks.py \
    --in-dir benchmark_RELEASE/dataset/cls \
    --out-dir benchmark_RELEASE/dataset/pngs

Start training:

python train.py --batch-size 2

Model checkpoints are saved under trained/, and can be used with the predict.py script for testing.

The training code is currently limited to the frontend module, and thus only outputs 16x16 segmentation maps. The augmentation pipeline does mirroring but not cropping or rotation.

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Fisher Yu and Vladlen Koltun, Multi-Scale Context Aggregation by Dilated Convolutions, 2016