Unique Class Count

Description

Pytorch implementation for ICLR 2020 paper "Weakly Supervised Clustering by Exploiting Unique Class Count"

Installation

# clone project
git clone https://github.com/howard-hou/UniqueClassCount
cd UniqueClassCount

# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda activate myenv

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

How to run

Download dataset first

mnist dataset is small, so it has been uploaded to github with the project.

for camelyon dataset, download the pre-processed dataset first, and put the dataset in data dir.

To see reproducing result

run notebook in notebooks dir

Train

Train mnist model with default configuration

# train
python ucc/train.py

Train camelyon model with chosen experiment configuration from configs/experiment/

python ucc/train.py experiment=camelyon.yaml