/DCC

This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), accepted at ICML 2022.

Primary LanguagePython

Dataset Condensation with Contrastive Signals

This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), published as a conference paper at ICML 2022. The implementation is based on (https://github.com/VICO-UoE/DatasetCondensation).

Prerequisites

  • pytorch (1.2.0)
  • numpy (1.15.1)
  • torchvision (0.4.0)
  • scipy (1.1.0)

Training and evaluation

To train the DCC (or DSAC) model in the paper, run this command:

python main.py --ipc <1, 10, or 50> --model ConvNet --dataset <CIFAR10, CIFAR100, or imagenet> (--imagenet_group <fine-grained dataset>) --method <DC or DSA> --contrast --save_path <save path name>

Please download ImageNet32x32 at (https://image-net.org/download-images)