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Data sourced from GitHub API

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  3. ageitgey
  4. face_recognition
PythonVisible projectStale activityNo linked package signalPartial snapshot

ageitgey/face_recognition

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Oct 25, 2026

The world's simplest facial recognition api for Python and the command line

Compare closest alternativesOpen GitHub
Stars
56.9K
Forks
13.7K
Daily momentum
—
Weekly momentum
—
30-day momentum
—

First read

Useful directional context, not a final verdict yet

ageitgey/face_recognition is better read as a directional signal than a clean recommendation. Keep the snapshot conservative and validate source activity, package reality, and close alternatives before committing to it.

Visible

56.9K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Jun 25, 2026.

Stale activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 56.9K stars
  • 13.7K forks
  • Last commit Jun 25, 2026
  • Package usage not mapped yet

Compare lens

google-ai-edge/mediapipe and huggingface/transformers are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Jun 25, 2026

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for ageitgey/face_recognition, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

face-detectionface-recognitionmachine-learningpython
🛡️ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines stable visibility.

Stable visibility
Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 56.9K
56.9K30d ago56.9K7d ago56.9K1d ago56.9KNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix
Current stars56.9K

Bar lengths use a log scale so stars and package downloads stay comparable.

Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Python repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

google-ai-edge/mediapipe

Shares the learning category footprint with ageitgey/face_recognition, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

huggingface/transformers

Shares the learning category footprint with ageitgey/face_recognition, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

pytorch/pytorch

Shares the learning category footprint with ageitgey/face_recognition, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

d2l-ai/d2l-zh

Shares the learning category footprint with ageitgey/face_recognition, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked mediapipe + transformers as the closest next comparison from the related repository set.

Open compare presetCompare with mediapipeCompare with transformers
Repo utility

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GitHub Repositoryageitgey/face_recognitionExport Ranking DataDownload CSV

Wider nearby ecosystem

Compare with mediapipeCompare with transformers
C++
37.4K
google-ai-edge/mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

Python
168.1K
huggingface/transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Python
105K
pytorch/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Python
82.1K
d2l-ai/d2l-zh

《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

Python
74.6K
openbq-org/OpenBB

Open Data Platform for analysts, quants and AI agents.

Python
73.3K
rohitg00/ai-engineering-from-scratch

Learn AI Engineering! Learn it. Build it. Ship it for others.

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of ageitgey/face_recognition based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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