CLTT stands for Contrastive Learning Through Time. It is the codebase used for the SVRHM NeurIPS workshop publication "Contrastive Learning Through Time" [1]. If you make use of this code please cite as follows:
[1] F. Schneider, X. Xu, M. R. Ernst, Z. Yu, and J. Triesch. Contrastive learning through time. In SVRHM 2021 Workshop @ NeurIPS, 2021.
- Clone the repository from here
- Make sure you have all the dependencies installed
- Download the corresponding TDW, COIL100 or Miyashita dataset files
- Configure the config.py file
- Start an experiment on your local machine with python3 main/train.py
The generator script for the Miyashita-style dataset can be found at github.com/mrernst/miyashita_fractals. Run the script with options --imgsize 64 --stimuli 100 to get the 100 fractals used for this work.
The ThreeDWorld (TDW) dataset will be available from Zenodo.
COIL-100 is publicly available at https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php
.
├── codetemplate # Template for collaboration purposes
├── config.py # Configuration for experiment parameters
├── data
│ ├── coil100_128x128 # COIL100 dataset
│ ├── fractals100_64x64 # Miyashita fractals
│ └── spherical_photoreal_64x64_DoF # ThreeDWorld dataset
├── img
│ └── header.png # Image that displays in README.md
├── LICENSE # MIT License
├── main
│ ├── generic.sbatch
│ └── train.py
├── README.md # ReadMe File
├── requirements.txt # conda/pip requirements
├── utils
│ ├── augmentations.py # augmentations for standard SimCLR
│ ├── datasets.py # dataloading and sampling
│ ├── evaluation.py # evaluation methods and analytics
│ ├── general.py # general utilities
│ ├── losses.py # definition of loss functions
└── └── networks.py # network definition, e.g. ResNet
Fork a copy of this repository onto your own GitHub account and clone your fork of the repository into your computer, inside your favorite folder, using:
git clone "PATH_TO_FORKED_REPOSITORY"
Install Python 3.9 and the conda package manager (use miniconda). Navigate to the project directory inside a terminal and create a virtual environment (replace <ENVIRONMENT_NAME>, for example, with cltt) and install the required packages:
conda create -n <ENVIRONMENT_NAME> --file requirements.txt python=3.9
Activate the virtual environment:
source activate <ENVIRONMENT_NAME>
This project is licensed under the MIT License - see the LICENSE file for details