https://github.com/minar09/u-net-attention
U-Net + Attention, extending U-Net model for semantic segmentation. Implemented with TensorFlow.
https://github.com/minar09/u-net-attention
attention attention-gate attention-model attention-to-scale encoder-decoder multi-scale semantic-segmentation tensorflow u-net
Last synced: about 1 month ago
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U-Net + Attention, extending U-Net model for semantic segmentation. Implemented with TensorFlow.
- Host: GitHub
- URL: https://github.com/minar09/u-net-attention
- Owner: minar09
- License: mit
- Created: 2019-03-06T02:11:31.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-11T16:57:40.000Z (about 6 years ago)
- Last Synced: 2025-03-25T06:51:12.725Z (about 2 months ago)
- Topics: attention, attention-gate, attention-model, attention-to-scale, encoder-decoder, multi-scale, semantic-segmentation, tensorflow, u-net
- Language: Python
- Homepage:
- Size: 63.5 KB
- Stars: 11
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fashion parsing models in TensorFlow
1. Tensorflow implementation of [Fully Convolutional Networks for Semantic Segmentation](http://arxiv.org/pdf/1605.06211v1.pdf) (FCNs).
2. TensorFlow implementation of [U-Net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/)
The implementation is largely based on the reference code provided by the authors of the paper [link](https://github.com/shelhamer/fcn.berkeleyvision.org).
1. [Prerequisites](#prerequisites)
2. [Training](#training)
2. [Testing](#testing)
2. [Visualizing](#visualizing)
## Prerequisites
- pydensecrf installation in windows with conda: `conda install -c conda-forge pydensecrf`. For linux, use pip: `pip install pydensecrf`.
- Check dataset directory in `read_dataset` function of corresponding data reading script, for example, for LIP dataset, check paths in `read_LIP_data.py` and modify as necessary.
## Training
- To train model simply execute `python FCN.py` or `python UNet.py`
- You can add training flag as well: `python FCN.py --mode=train`
- `debug` flag can be set during training to add information regarding activations, gradients, variables etc.
## Testing
- To test and evaluate results use flag `--mode=test`
- After testing and evaluation is complete, final results will be printed in the console, and the corresponding files will be saved in the "logs" directory.
## Visualizing
- To visualize results for a random batch of images use flag `--mode=visualize`