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Topics: metric-learning

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2 repositories by Attention Score · Aug 5, 2026 – Nov 2, 2026 (UTC)

Language: Python
Topic: metric-learning
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1.catalyst-team / catalyst

Accelerated deep learning R&D

deep-learning
reinforcement-learning
machine-learning
computer-vision
pytorch
python
+14 more

Python APACHE-2.03.4K total396 forks

+10 stars

from 3,378 · score 2.40

2.scikit-learn-contrib / metric-learn

Metric learning algorithms in Python

python
metric-learning
scikit-learn
machine-learning

Python MIT1.4K total234 forks

+2 stars

from 1,428 · score 1.10

Order: Attention Score

Most net stars gained in the window first. The score only puts this gain on a log scale, so the order is the order of the gain.

How the ranking works

Ranked by stars gained in the window. The score is the natural log of one plus that gain, so a repository's rank is exactly its star gain's rank.

score = log1p(net stars gained in the trailing T complete UTC days)

A repository is ranked only when its Star History covers every day of the window and its latest Observation is not missing.

The longest window is 180 days. Star History from the Repository Host goes back at most 180 days before a repository's first Observation. A longer window needs more days of Observations first. A repository younger than the window is not ranked in it.

The filters narrow the same eligible repositories. A repository matches the topics when it has any of them. Total stars is the count now. Last push and creation on the Repository Host count back from the start of today (UTC). The counts beside the languages, licenses, and topics include all ranked repositories in the window. A Tag holds the repositories that have it; see Tags.

Repositories enter the index from the Seed Dataset or by gaining stars faster than the published admission threshold, whatever their size. Admission is permanent, so an archived repository can still rank.

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