https://github.com/nicolov/segmentation_keras
DilatedNet in Keras for image segmentation
https://github.com/nicolov/segmentation_keras
caffe deeplearning keras segmentation tensorflow
Last synced: about 1 month ago
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DilatedNet in Keras for image segmentation
- Host: GitHub
- URL: https://github.com/nicolov/segmentation_keras
- Owner: nicolov
- License: mit
- Created: 2016-11-21T22:15:04.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-01-05T15:01:43.000Z (over 6 years ago)
- Last Synced: 2024-11-05T16:49:12.052Z (6 months ago)
- Topics: caffe, deeplearning, keras, segmentation, tensorflow
- Language: Python
- Size: 158 KB
- Stars: 301
- Watchers: 17
- Forks: 92
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
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'](https://github.com/fyu/dilation).
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](http://home.bharathh.info/pubs/codes/SBD/download.html):
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.
*Fisher Yu and Vladlen Koltun, Multi-Scale Context Aggregation by Dilated Convolutions, 2016*