The Pipeline
How Kestrel Sees Your Photos.
Project Kestrel doesn't just "guess" which photos are good. It follows a multi-stage machine learning pipeline to decompose your photography into objective data.
Phase 1
Intelligent Scene Grouping
Kestrel compares your images with each other to identify high-speed bursts and groups them into scenes. By grouping these into scenes, you can compare nearly identical frames against each other using Kestrel's quality algorithms.
Phase 2
Subject Detection & Masking
Using a specialized object detection model, Kestrel identifies birds within the frame. It then creates precise masks around the subjects, ensuring that background detail never influences the quality score.
Phase 3
Objective Quality Analysis
Kestrel's machine learning model is trained to take into account noise, motion blur, and sharpness to create a normalized quality score that can accurately differentiate between each frame in a scene.
Phase 4
Species Classification (Beta)
Kestrel automatically tags your photos by species and family. This powerful metadata allows you to search through tens of thousands of photos instantly to re-discover your favorite birding memories.
Design Philosophy
Computers can never replace artistic vision.
Project Kestrel is built on the belief that AI should be a cofactor, not a replacement. Kestrel handles the "boring" parts—calculating sharpness and grouping bursts—so you can focus on the artistic decisions: composition, lighting, and story.
100% Local. 100% Yours. All models run on your CPU/GPU. No images are ever sent to a server.