https://github.com/minar09/fashion-clothing-parsing
FCN, U-Net models implementation in TensorFlow for fashion clothing parsing
https://github.com/minar09/fashion-clothing-parsing
clothing clothing-parsing fashion fashion-parsing fcn fully-convolutional-networks parsing segmentation semantic-segmentation tensorflow u-net
Last synced: 2 months ago
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FCN, U-Net models implementation in TensorFlow for fashion clothing parsing
- Host: GitHub
- URL: https://github.com/minar09/fashion-clothing-parsing
- Owner: minar09
- License: mit
- Created: 2019-01-02T01:23:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-14T04:49:02.000Z (about 5 years ago)
- Last Synced: 2025-03-25T06:51:12.744Z (3 months ago)
- Topics: clothing, clothing-parsing, fashion, fashion-parsing, fcn, fully-convolutional-networks, parsing, segmentation, semantic-segmentation, tensorflow, u-net
- Language: Python
- Homepage:
- Size: 1.23 MB
- Stars: 40
- Watchers: 3
- Forks: 15
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fashion parsing models in TensorFlow
This is the source code of our project for Fashion Clothing Parsing. (EMCOM Lab, SeoulTech, Korea)
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/)
3. Improved networks based on 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. [Dataset](#dataset)
3. [Training](#training)
4. [Testing](#testing)
5. [Visualizing](#visualizing)
6. [CRF](#crf)
7. [BFSCORE](#bfscore)
## Directory Structure
```bash
├── parseDemo20180417
│ └── clothparsing.py
├── tests
│ ├── __init__.py
│ ├── gt.png
│ ├── inference.py
│ ├── inp.png
│ ├── output.png
│ └── pred.png
│ └── test_crf.py
│ └── test_labels.py
└── .gitignore
└── __init__.py
└── BatchDatasetReader.py
└── bfscore.py
└── CalculateUtil.py
└── denseCRF.py
└── EvalMetrics.py
└── FCN.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
└── UNet.py
└── UNetAttention.py
└── UNetMSc.py
└── UNetPlus.py
└── UNetPlusMSc.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 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.
- Set your hyper-parameters in the corresponding model script
## 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.
- 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