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
2. Dataset
3. Training
4. Testing
5. Visualizing
6. CRF
7. BFSCORE

└── __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

• 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.

• Right now, there are dataset supports for 3 datasets. Set your directory path in the corresponding dataset reader script.
• CFPD (For preparing CFPD dataset, you can visit here: https://github.com/minar09/dataset-CFPD-windows)
• 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)

• 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

• 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

• To visualize results for a random batch of images use flag --mode=visualize
• Set your hyper-parameters in the corresponding model script

• Running testing will apply CRF by default.
• If you want to run standalone, run python denseCRF.py, after setting your paths.

• Run python bfscore.py, after setting your paths.
• For more details, visit https://github.com/minar09/bfscore_python