Semantic-Search on RawCull

How PhotoAIKit Is Constructed

Thu, 30 Jul 2026 00:00:00 +0000

How PhotoAIKit Is Constructed

PhotoAIKit is a reusable Swift package extracted from application code. Its most important achievement is not merely that CLIP and SAM 3 run. It is that reusable AI behavior has been separated from RawCull’s UI, RAW-file handling, paths, sandbox rules, and culling policy.

This document explains the construction from the bottom up and gives the reason for each boundary.

1. Begin With The Package Boundary

The package owns:

How RawCull Loads and Uses CLIP

Wed, 29 Jul 2026 00:00:00 +0000

How RawCull Loads and Uses CLIP

RawCull supports two CLIP models:

OpenAI CLIP ViT-B/32, which processes a 224 × 224 image;
OpenCLIP DataComp ViT-B-32-256, which processes a 256 × 256 image.

The user selects one model in Settings > AI. That one selection defines the vector space used for both image-to-image similarity and text-to-image semantic search. RawCull never mixes vectors from the two models.

This page starts with the basic idea behind CLIP and then follows the current source code from application startup, through model validation and Core AI inference, to search results, burst groups, and persisted artifacts.

CLIP Model Evaluation Results

Wed, 12 Aug 2026 00:00:00 +0000

CLIP Model Evaluation Results

Report date: 2026-08-12

This report compares the product-behavior results produced by RawCullFB for:

OpenAI CLIP ViT-B/32 at 224 × 224; and
OpenCLIP DataComp ViT-B/32-256 using the datacomp_s34b_b86k checkpoint.

The runs follow the product-behavior portion of Evaluating CLIP Models. They cover the canonical 77 semantic queries and a complete all-pairs image-similarity pass over 453 indexed images.

The reports show that both integrations completed cleanly, neither model shows semantic collapse, and both produce coherent image-neighbourhood structures. DataComp is modestly faster and more consistent across paraphrases. Those are promising sanity-check results, but they are not labeled accuracy metrics.