By Chuang Niu and Ge Wang

This is a Pytorch implementation of the paper. (In updating)

• SOTA on 5 benchmarks. Please refer to Papers With Code for Image Clustering

Please refer to requirement.txt for all required packages. Assuming Anaconda with python 3.7, a step-by-step example for installing this project is as follows:

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
conda install -c conda-forge addict tensorboard python-lmdb
conda install matplotlib scipy scikit-learn pillow

Then, clone this repo

git clone https://github.com/niuchuangnn/SPICE.git
cd SPICE

Prepare datasets of interest as described in dataset.md.

Read the training tutorial for details.

Evaluation of SPICE-Self:

python tools/eval_self.py --config-file configs/stl10/eval.py --weight PATH/TO/MODEL --all 1

Evaluation of SPICE-Semi:

python tools/eval_semi.py --load_path PATH/TO/MODEL --net WideResNet --widen_factor 2 --data_dir PATH/TO/DATA --dataset cifar10 --all 1 

Read the evaluation tutorial for more descriptions also including the visualization.

All trained models in our paper are available as follows.

More models based on ResNet18 for both SPICE-Self* and SPICE-Semi*.

• GATCluster
• MOCO
• SCAN
• FixMatch
• IIC