CLTT

Contrastive Learning Through Time

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.

Getting started with the repository

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

Prerequisites

Datasets

Miyashita

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.

TDW

The ThreeDWorld (TDW) dataset will be available from Zenodo.

COIL-100

COIL-100 is publicly available at https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php

Directory structure

.
├── 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

Installation guide

Forking the repository

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"

Setting up the environment

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>

License

This project is licensed under the MIT License - see the LICENSE file for details

trieschlab/CLTT