pinglmlcv / SRe2L

Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves highest 60.8% on original ImageNet-1K val set.

SRe2L

Official PyTorch implementation of paper:

"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective"
Zeyuan Yin, Eric Xing, Zhiqiang Shen
MBZUAI, CMU

[Project Page] [Paper]

Abstract

We present a new dataset condensation framework termed Squeeze (), Recover () and Relabel () (SRe²L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying scales of datasets, model architectures and image resolutions for effective dataset condensation. The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with high-resolution training, and the ability to scale up to arbitrary evaluation network architectures. Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8% validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively. Our approach also outperforms MTT by approximately 52× (ConvNet-4) and 16× (ResNet-18) faster in speed with less memory consumption of 11.6× and 6.4× during data synthesis.

Distillation Animation

---

Kindly wait a few seconds for the animation visualizations to load.

---

Distilled ImageNet

Squeeze

• For ImageNet-1K, we use the official PyTorch pre-trained models from Torchvision Model Zoo.

• For Tiny-ImageNet-200, we use official Torchvision code to train the model from scratch.

Recover

More details in recover/README.md.

cd recover
sh recover.sh

Relabel

More details in relabel/README.md.

cd relabel
sh relabel.sh

Train on Distilled Data

More details in train/README.md.

cd train
sh train.sh

Download

You can download distilled data and soft labels from https://zeyuanyin.github.io/projects/SRe2L/#Download.

Results

Our Top-1 accuracy (%) under different IPC settings on Tiny-ImageNet and ImageNet-1K datasets:

Citation

If you find our code useful for your research, please cite our paper.

@article{yin2023squeeze,
	title = {Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective},
	author = {Yin, Zeyuan and Xing, Eric and Shen, Zhiqiang},
	journal = {arXiv preprint arXiv:2306.13092},
	year = {2023}
}