https://github.com/minar09/asu-net
ASU-Net: Attention to Scale with U-Net for Semantic Segmentation (Implemented with TensorFlow)
https://github.com/minar09/asu-net
asu-net attention attention-to-scale clothing-parsing fashion-parsing segmentation semantic-segmentation tensorflow u-net
Last synced: about 2 months ago
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ASU-Net: Attention to Scale with U-Net for Semantic Segmentation (Implemented with TensorFlow)
- Host: GitHub
- URL: https://github.com/minar09/asu-net
- Owner: minar09
- License: mit
- Created: 2019-05-12T13:27:43.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-15T06:31:24.000Z (about 6 years ago)
- Last Synced: 2025-03-25T06:51:12.555Z (2 months ago)
- Topics: asu-net, attention, attention-to-scale, clothing-parsing, fashion-parsing, segmentation, semantic-segmentation, tensorflow, u-net
- Language: Python
- Size: 49.8 KB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ASU-Net: Attention to Scale with U-Net for Semantic Segmentation
This is a TensorFlow implementation of our ASU-Net. We train Multi-scale U-Net model first, and then the ASU-Net.
For more details, check our paper. (Link coming soon)
1. [Prerequisites](#prerequisites)
2. [Dataset](#dataset)
3. [Training](#training)
4. [Testing](#testing)
5. [Visualizing](#visualizing)
6. [CRF](#crf)
7. [BFSCORE](#bfscore)
## Directory Structure
```bash
└── __init__.py
└── .gitignore
└── ASUNet.py
└── BatchDatasetReader.py
└── bfscore.py
└── CalculateUtil.py
└── denseCRF.py
└── EvalMetrics.py
└── function_definitions.py
└── LICENSE
└── read_10k_data.py
└── read_CFPD_data.py
└── read_LIP_data.py
└── README.md
└── requirements.txt
└── TensorflowUtils.py
└── test_human.py
└── UNetMSc.py
```
## Prerequisites
- For required packages installation, run `pip install -r requirements.txt`
- 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.
## Dataset
- Right now, there are dataset supports for 3 datasets. Set your directory path in the corresponding dataset reader script.
- [CFPD](https://github.com/hrsma2i/dataset-CFPD) (For preparing CFPD dataset, you can visit here: https://github.com/minar09/dataset-CFPD-windows)
- [LIP](http://www.sysu-hcp.net/lip/)
- 10k (Fashion)
- If you want to use your own dataset, please create your dataset reader. (Check `read_CFPD_data.py` for example, on how to put directory and stuff)
## Training
- To train model simply execute `python UNetMSc.py` and then `python ASUNet.py`
- You can add training flag as well, for example: `python UNetMSc.py --mode=train` and `python ASUNet.py --mode=train`
- `debug` flag can be set during training to add information regarding activations, gradients, variables etc.
- Set your hyper-parameters in the corresponding model script
## Testing
- To test and evaluate results use flag `--mode=test`, e.g., `python ASUNet.py --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.
- Set your hyper-parameters in the corresponding model script
## Visualizing
- To visualize results for a random batch of images use flag `--mode=visualize`
- Set your hyper-parameters in the corresponding model script
## CRF
- Running testing will apply CRF by default.
- If you want to run standalone, run `python denseCRF.py`, after setting your paths.
## BFSCORE
- Run `python bfscore.py`, after setting your paths.
- For more details, visit https://github.com/minar09/bfscore_python