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