Ai 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.

AI Model Download Service

Mon, 10 Aug 2026 00:00:00 +0000

AI model download service

RawCull supports exactly three optional model bundles:

Model	PhotoAIKit bundle	Runtime asset	Use
DataComp CLIP	`CLIP-DataComp`	`ViT-B-32-256-datacomp_s34b_b86k_float16_static.aimodel`	Similarity and semantic search
OpenAI CLIP	`CLIP-OpenAI`	`clip-vit-base-patch32_float16_static.aimodel`	Similarity and semantic search
Meta SAM 3	`SAM3`	`sam3_float16.aimodel`	Promptable segmentation

SigLIP2 and EfficientSAM are not part of the RawCull download catalogue or release manifest. Do not include their bundles, notices, archives, or asset-pack IDs in a RawCull model release.

AI Model Licence

Sun, 02 Aug 2026 00:00:00 +0000

AI model licence and provenance clearance procedure

Status: publication hold
Last reviewed: 2026-08-03

Purpose and current decision

This document defines how RawCull clears the DataComp CLIP, OpenAI CLIP, and Meta SAM 3 model packs for public download. It covers technical provenance, licence evidence, upstream contacts, questions to ask, acceptable answers, and the final release gate.

No .aar model archive has been uploaded. Do not create a public GitHub Release, publish a download manifest, or change a production descriptor to ready until every model is either:

Evaluating CLIP Models

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

Evaluating CLIP Models Against Source-Framework Cosine Values and RawCullFB

This document is the complete repeatable procedure for validating CLIP model bundles used by RawCullFB. It covers both supported candidates:

OpenAI CLIP ViT-B/32 at 224 × 224.
OpenCLIP DataComp ViT-B/32-256 with datacomp_s34b_b86k weights.

SigLIP2 is outside the current evaluation scope.

The procedure has two independent validation layers:

Numerical parity: compare embeddings produced by the Core AI bundle with reference embeddings produced by the original Hugging Face Transformers or OpenCLIP implementation.
Product behavior: use RawCullFB to build a real catalog index, execute the same 77 semantic queries, inspect image-similarity neighborhoods, and measure labeled retrieval quality.

A model must pass both layers. Numerical parity proves that the conversion and integration reproduce the source model; it does not prove that the model retrieves useful photographs. Conversely, plausible search results do not prove that the converted model is correct.

New RawCull AI models

Mon, 10 Aug 2026 00:00:00 +0000

Publishing new RawCull AI models

This runbook publishes exactly three optional models:

Model	Asset-pack ID	Installed destination
DataComp CLIP	`no.blogspot.RawCull.models.clip-datacomp`	`Models/CLIP-DataComp`
OpenAI CLIP	`no.blogspot.RawCull.models.clip-openai`	`Models/CLIP-OpenAI`
Meta SAM 3	`no.blogspot.RawCull.models.sam3`	`Models/SAM3`

SigLIP2 and EfficientSAM are deliberately excluded. Do not add them to the release staging tree, packaging manifests, download manifest, production catalogue, settings UI, notices, or tests.

The examples use the v2 release in RawCull-AI-Models. Tag names and GitHub release URLs are case-sensitive. If another tag is chosen, replace v2 everywhere and keep both RawCull manifest URLs identical.

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.

RawCull Packages

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

RawCull Packages

RawCull is split across four reusable Swift packages. Each package owns one kind of knowledge and deliberately avoids importing the others. RawCull is the composition root: it decodes a file and capture metadata with RawParserKit, asks PhotoAnalysisKit or PhotoAIKit to measure or index it, translates the evidence into RawCullCore values and decisions, and presents or persists the outcome.

flowchart LR
 Files["ARW, NEF, and rendered files"] --> Parser["RawParserKit\ndecode + capture metadata"]
 Parser --> Host["RawCull adapters\nsource and cache policy"]
 Host --> Analysis["PhotoAnalysisKit\nsharpness + focus evidence"]
 Host --> AI["PhotoAIKit\nimage/text embeddings + segmentation"]
 Analysis --> Host
 AI --> Host
 Host --> Core["RawCullCore\nburst grouping + ranking"]
 Core --> App["RawCull UI, persistence, and culling actions"]

The arrows are runtime data flow, not package dependencies. The four sibling packages do not depend on one another; the RawCull application imports and adapts them.

Artificial Intelligence

Sat, 18 Jul 2026 00:00:00 +0000

Artificial Intelligence in RawCull

This section explains how RawCull uses reusable AI components without allowing model-runtime details to spread through the application. It is written as a learning path: first understand the boundary between RawCull and PhotoAIKit, then study the package itself, and finally follow CLIP from application startup to a persisted similarity artifact.

The source for this section comes from the sibling PhotoAIKit and RawCull projects. Paths beginning with Sources/ refer to PhotoAIKit. Paths beginning with Model/, Main/, Views/, or Actors/ refer to the RawCull app target.