This is a code implementation of the paper .
Installation
$ git clone https://github.com/stefanherdy/unsupervised-invariant-information-clustering.git
Usage
- First, add your custom datasets to the input_data folder
- Run sem_seg_test.py
You can specify the following parameters:
--learnrate, type=int, default=0.001, help='learn rate of optimizer"
--epochs, type=int, default=500
--batch_size, type=int, default=2, help="Batch Size"
--num_layers, type=int, default=32, help="Number of UNet layers"
--num_blocks, type=int, default=1, help="Number of UNet blocks"
--resize, type=int, default=512, help="Image size for resizing"
Example usage:
"python train.py --batch_size 4 --learnrate 0.0001 --resize 1024
- optimize your hyperparameters
License
This project is licensed under the MIT License. ©️ 2023 Stefan Herdy