jgraving / segmentation_keras

DilatedNet in Keras for image segmentation

Keras implementation of DilatedNet for semantic segmentation

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 requires Python 3.5.

Testing

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

pip install -r requirements.txt
pip install tensorflow-gpu

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

Training

Download the Augmented Pascal VOC dataset here. Use the convert_masks.py script to convert the provided masks in .mat format to RGB pngs:

python convert_masks.py \
    --in-dir /mnt/pascal_voc/dataset/cls \
    --out-dir /mnt/pascal_voc/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