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

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Deep Learning computer-vision representation-learning self-supervised-learning
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"
}
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Comments
  • Part 3, 4 are missing

    Part 3, 4 are missing

    Hello @yukimasano! đŸ‘‹đŸ»

    I was trying to download the dataset, but found part 3 and 4 were missing. I guess I can download the images from the AWS url but I prefer to download them in zip files. Would you mind uploading the missing parts?

    Thanks so much for sharing this amazing dataset!

    opened by junhsss 1
  • tags / labels for the images

    tags / labels for the images

    Hello guys,

    Thanks so much for releasing a large scale dataset for open-source community, even for commercial use. I was wondering if there are also some tags associated with each image, like say 'indoor', 'outdoor', 'beach', 'sports' etc. These tags would be very helpful in selecting subsets of images based on the task for training.

    Thanks!

    opened by ankuPRK 1
  • Sharing Pretrained models with the Hugging Face Hub

    Sharing Pretrained models with the Hugging Face Hub

    Hi @yukimasano !

    The work you're doing with PASS is very exciting.

    I see you currently have models hosted in your own server. Would you be interested in also sharing your models in the Hugging Face Hub? The Hub offers free hosting of over 25K models, and it would make your work more accessible and visible to the rest of the ML community.

    Some of the benefits of sharing your models through the Hub would be:

    • wider reach of your work to the ecosystem
    • versioning, commit history and diffs
    • repos provide useful metadata about their tasks, languages, metrics, etc that make them discoverable
    • multiple features from TensorBoard visualizations, PapersWithCode integration, and more

    Creating the repos and adding new models should be a relatively straightforward process if you've used Git before. This is a step-by-step guide explaining the process in case you're interested. Please let us know if you would be interested and if you have any questions.

    Happy to hear your thoughts, Omar and the Hugging Face team

    cc @nateraw

    opened by osanseviero 2
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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