Model Hubs & Serving

PASS

Self-supervised pretraining without human labels for computer vision tasks.

Status
UnmaintainedNo commits in 1542 days.
Last checked
Aging · Sep 1, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

PASS is an open-source model designed to replace ImageNet for self-supervised learning in computer vision, eliminating the need for human-labeled data. It's particularly useful for researchers and developers looking to train models with minimal supervision.

PASS stands out by offering a robust solution for self-supervised pretraining in computer vision without the need for human-labeled data, making it ideal for researchers and developers focused on unsupervised learning techniques.

When it fits — and when it doesn’t

A good fit for

  • Teams working on self-supervised learning projects who need a reliable pretraining dataset without human labels.

  • Research groups exploring new methods in unsupervised and semi-supervised learning.

Look elsewhere if

  • Projects requiring high accuracy with minimal training data, as PASS is designed for large-scale self-supervised learning.

  • Applications that strictly require labeled datasets for supervised learning tasks.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited integrations with other libraries and frameworks

    Documentation lacks examples for integration with popular computer vision tools like TensorFlow or PyTorch

  2. 02

    Performance issues on large datasets

    Benchmarking shows slower processing times compared to ImageNet when handling high-resolution images

What it does well

  • Self-supervised learning without human labels

  • Designed to replace ImageNet for pretraining tasks

  • Open-source and MIT licensed

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up PASS

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →