The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Overview

PASS: Pictures without humAns for Self-Supervised Pretraining

TL;DR: An ImageNet replacement dataset for self-supervised pretraining without humans

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Content

PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information that can be used for high-quality pretraining while significantly reducing privacy concerns.

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Download the dataset

Generally: all information is on our webpage.

For downloading the dataset, please visit our dataset on zenodo. There you can download it in tar files and find the meta-data.

You can also download the images from their AWS urls, from here.

Pretrained models

Pretraining Method Epochs Places205 lin. Acc. Model weights
IN-1k MoCo-v2 200 50.1 R50 weights
PASS MoCo-v2 200 52.8 R50 weights
PASS MoCo-v2-CLD 200 53.1 R50 weights
PASS SwAV 200 55.5 R50 weights
PASS DINO 100 X ViT S16 weights
PASS DINO 300 coming soon
PASS MoCo-v2 800 coming soon

Contribute your models

Please let us know if you have a model pretrained on this dataset and I will add this to the list above.

Citation

@Article{asano21pass,
author = "Yuki M. Asano and Christian Rupprecht and Andrew Zisserman and Andrea Vedaldi",
title = "PASS: An ImageNet replacement for self-supervised pretraining without humans",
journal = "NeurIPS Track on Datasets and Benchmarks",
year = "2021"
} 
Owner
Yuki M. Asano
I'm an Computer Vision researcher at the University of Amsterdam. Did my PhD at the Visual Geometry Group in Oxford.
Yuki M. Asano
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