ncoevoet

ncoevoet / facet

Public

A multi-dimensional photo analysis engine that examines every facet of an image — from aesthetic appeal and composition to facial detail and technical precision — using an ensemble of vision models to surface the photos that truly shine.

61
7
100% credibility
Found Feb 17, 2026 at 27 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Facet is an AI-powered photo analysis tool that scores images for aesthetic quality, composition, technical metrics, recognizes faces, and provides an interactive web gallery for browsing and organizing photos.

How It Works

1
📸 Gather your photos

Point the tool to your folder of personal photos from trips, family events, or anywhere.

2
🔍 Let it analyze everything

It quietly examines each photo for beauty, sharpness, faces, and creative patterns while you relax.

3
✨ Discover your smart gallery

Open a sleek dark gallery that shows infinite scrolling previews with quality scores on hover.

4
📊 Sort and filter easily

Browse by top scores, dates, cameras, or moods, hiding blinks and bursts with simple chips.

5
👥 Recognize familiar faces

Automatically groups faces into people you can name, merge, or organize.

6
📈 Explore stats and insights

Check dashboards for gear performance, timelines, and what makes your best shots shine.

🌟 Your best photos rise to the top

Effortlessly find, favorite, and organize the images that truly capture your moments.

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Star Growth

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AI-Generated Review

What is facet?

Facet is a Python-based photo analysis engine that scores images across aesthetics, composition, faces, and technical quality using vision models like TOPIQ and CLIP. Point it at your photo library via CLI (`python photos.py /path/to/photos`), and it builds a SQLite database with detailed metrics, then spins up a dark-themed web gallery (`python viewer.py`) for sorting by 24 metrics, filtering 50+ ways, and infinite-scroll browsing. It solves the pain of manually culling thousands of shots by surfacing keepers with face clustering, burst detection, and EXIF integration—far beyond basic tools like facet .net github or github facet rust alternatives.

Why is it gaining traction?

Its ensemble approach delivers nuanced multi-dimensional photo analysis, auto-detecting VRAM for optimal model loading (2GB legacy to 24GB beasts), plus pairwise comparisons that learn from your preferences to tweak weights. The responsive gallery shines with hover score breakdowns, stats dashboards, and person management UI, making it addictive for exploration. Developers dig the no-setup Docker support and config-driven categories for portraits, landscapes, or astro shots.

Who should use this?

Photographers drowning in RAW/JPEG libraries need it for automated ranking and face grouping. Indie devs building gallery apps or multi-dimensional integration photos tools will fork the scoring pipeline. Hobbyists tweaking aesthetic analysis on NAS setups via Synology deployment docs.

Verdict

Try it if you have 1k+ photos—docs are thorough, CLI is polished, but 19 stars and 1.0% credibility signal early-stage maturity; expect some GPU tweaks. Solid foundation for personal use, watch for wider adoption.

(198 words)

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