AI in RawCull
Start with the AI review workflow, then explore local models, downloads, and licences.
RawCull uses local AI at two stages: fast CLIP indexing across the catalog, followed by SAM 3 and Qwen review of selected candidates. Ordinary browsing and rating do not require the optional models.
- AI Step by Step is the practical guide: narrow down a catalog, choose finalists, and review them in each AI tab.
- AI Analysis explains model roles, catalog indexing, downloads, and licences.
- AI Screenshots shows the controls and results for all three tabs.
For catalog grouping and search, start with Similarity, Bursts, and Search.
1 - AI Step by Step
A practical workflow for using Burst Review, SAM 3 with CLIP, Qwen Vision, and Objects to review selected photographs.
RawCull uses three local AI models. Each has a different job:
| Model | What it is | What it contributes |
|---|
| CLIP | An image-and-text embedding model | Recognizes visual similarity, helps form burst groups, and can identify a broad subject such as a person, bird, or animal. |
| SAM 3 | A prompt-guided segmentation model | Draws a mask around the subject so RawCull can measure detail on the important part of the photograph instead of the whole frame. |
| Qwen Vision | A vision-language model | Reviews the visible photograph and comments on composition, exposure, subject visibility, expression, obstructions, strengths, and problems. |
In Deep Review, SAM 3 and CLIP work together. CLIP suggests what the
subject is; SAM 3 uses that information to find the subject; RawCull then
measures detail inside the mask. If CLIP cannot supply a useful label, SAM 3
can still use the general Subject prompt.
All three models run on the Mac. They advise you; they do not replace your
decision. Before starting, install the models you want under Settings › AI;
SAM 3 also requires acceptance of its licence.
Why analyze only a small set with AI?
CLIP runs quickly enough to index every photograph in the catalog for similarity and semantic search. Its cached embeddings can be reused without processing each image again for every search.
Detailed AI review is much heavier than ordinary thumbnail browsing. SAM 3
must create a subject mask for each photograph, while Qwen examines and
describes one photograph at a time. Running both over every file would spend
time on obvious rejects and repeated frames.
RawCull therefore uses a funnel:
- Burst Review quickly reduces the complete catalog.
- You keep a small set by selecting images or rating them with two or more stars.
- AI Analysis gives those finalists a closer review.
This is both faster and more useful: AI spends its time on the difficult
choices. For a SAM 3 + CLIP batch larger than 12 images, RawCull reviews at
most eight candidates, using the available burst ranking or the current file
order.
1. Begin with Burst Review
Open a catalog, go to Similarity, and choose Analyze Bursts. Open a group from Needs Review to compare its frames in the burst reviewer.
RawCull performs three steps:
- Similarity: CLIP creates a numeric image embedding and groups
near-duplicates. If CLIP is unavailable, RawCull can use Apple Vision for
similarity instead.
- Sharpness: RawCull measures focus and useful detail. This is traditional
image analysis, not a generative AI opinion.
- Ranking: similarity and sharpness evidence are combined to suggest the
strongest frames in each burst.
Start with Needs review. Compare the best-ranked frames, check important
details at a useful zoom level, and make your own decision. Select the
remaining candidates in Grid View, or rate promising images with two or more
stars so that they appear as Tagged images later.
2. Choose the finalists
Open AI Analysis from the toolbar. At the top right, choose one input:
- Selected uses the images currently selected in Grid View.
- Tagged uses every active-catalog image rated two stars or higher.
Keep this set small. A handful of close candidates is ideal.
3. Run SAM 3 + CLIP
Choose SAM 3 + CLIP, then select a review target:
- Auto lets the detected subject guide the mask.
- Full Subject checks detail across the complete subject.
- Head / Face concentrates on the area that often decides portraits and wildlife photographs.
Choose Run Deep Review. For each candidate, inspect the subject outline,
the Deep score, the normal Sharp score, mask status, AF position, and
any warning in Notes. A high score is useful only when the mask covers the
subject you intended. If the outline is wrong, trust the photograph—not the
number.
Use this review to answer: Which frame contains the best detail on the
subject that matters?
4. Run Qwen Vision
Choose Qwen Vision. The default prompt asks about composition, exposure,
subject visibility, expression, and obstructions. You may replace it with a
specific question, for example: Which visible problems would matter in a
final edit?
Choose Run Analyze. Qwen processes the pending images one at a time and
returns scores plus strengths and issues. It may also report whether eyes are
open when that can be judged from the image.
Use this review to answer: Does the photograph work as a photograph? Qwen
adds a visual critique; it does not participate in burst similarity or SAM 3
subject-detail scoring.
5. Run AI Objects
Choose Objects and keep Selected or Tagged as the input. SAM 3 and Qwen must both show as ready.
- Leave Concepts on Automatic for Qwen to suggest concrete subjects in each photograph. If you already know what to look for, choose Specific Concepts and enter short comma-separated terms such as
puffin or deer, fawn. - Optionally edit the additional photographic criteria, then choose Analyze [number] Images to process the pending photos. You can cancel a running batch, retry failed results, or clear results from this view.
- Select a completed row. Compare the numbered outlines with the original image, then choose an object in the list to inspect its crop and Qwen description. The table also shows discovered concepts, object count, Qwen assessment confidence, and status.
- Read the whole-photo summary, per-object visibility and focus notes, relationships, strengths, and problems. Treat the SAM 3 mask score and Qwen assessment confidence as different signals. Check the crop, outline, and wording against the source photo; small or overlapping subjects can be missed or mixed up.
Objects is useful when a frame contains several subjects, such as a deer with fawns or a group of musk oxen. Its numbered crops help you inspect each subject, but the analysis remains advisory. It does not rate, reject, or select a photograph for you.
6. Make the final choice
Use each result to answer a different question:
- Your visual judgment: does the pose, timing, and framing match your intent?
- SAM 3 + CLIP: is the intended subject masked correctly, and which candidate has useful subject detail?
- Objects: are the individual subjects visible, and do their outlines and crops match the photograph?
- Qwen Vision: which composition or visibility issues deserve a closer look?
- Burst Review: which neighboring frames are worth comparing before you decide?
When the signals disagree, inspect the original preview at a useful zoom level. Focus-map overlap and model confidence do not prove sharpness or photographic quality. Ratings and final picks remain your decision; RawCull does not automatically turn a Qwen result into a rating.
For catalog grouping, see Similarity, Bursts, and Search. For models and downloads, see AI Analysis. The AI Screenshots tour shows the results in each tab.
2 - AI Analysis
RawCull requires macOS 27 (Golden Gate) and an Apple Silicon Mac.
RawCull provides optional local AI for search and review. All inference runs on the Mac; RawCull does not upload photographs to an external AI service.
How the Models Are Used
RawCull uses three vision models:
- DataComp CLIP converts images and text into comparable vectors. RawCull uses those vectors to index every ARW file in a catalog for semantic search, visual similarity, and burst grouping.
- SAM 3 locates subjects in selected images. Deep Review uses a subject mask together with sharpness, CLIP, and camera autofocus evidence; Objects keeps separate masks for individual visible instances.
- Qwen3-VL assesses selected images against editable criteria. In Objects mode it can suggest object concepts and describe the numbered objects, their relationships, strengths, and possible problems.
CLIP is compact and fast enough to index all ARW files in a catalog. SAM 3 and Qwen are substantially larger and require more computation, so RawCull reserves them for deeper analysis of selected photographs rather than running them across the complete catalog.
AI results are review aids, not automatic decisions. Semantic-search scores describe relative similarity rather than confidence, and the photographer always makes the final selection.
Catalog-Wide CLIP Indexing
DataComp CLIP does not create captions, keywords, or fixed labels while indexing. It converts each image into a normalized numeric embedding that summarizes its overall visual content. RawCull stores that embedding locally and can reuse it for both similarity analysis and semantic search.
Enable DataComp CLIP in Settings > AI, then select Index Similarity or Re-index in Similarity view. Indexing runs the image encoder once for each ARW photograph that does not already have a compatible cached embedding. This catalog-wide index supports both similarity and semantic search without invoking the larger SAM 3 or Qwen models.
CLIP embeddings are specific to the model and its preprocessing configuration. Installing an incompatible model version requires a new index. Similarity and semantic search require compatible DataComp CLIP embeddings.
See Similarity, Bursts, and Search for the catalog workflow.
Deeper Analysis of Selected Photographs
Select photographs in Grid View, or use photographs rated two stars and higher, then open AI Analysis. This focused workflow avoids the time and computational cost of running the larger models on every ARW file in the catalog.
- SAM 3 + CLIP isolates the subject and combines subject-aware detail, sharpness, autofocus, and coverage evidence to rank the selected photographs and recommend a frame.
- Qwen Vision evaluates each selected photograph against editable criteria such as composition, exposure, subject visibility, expression, and obstructions. It returns an advisory assessment with scores, strengths, possible problems, confidence, and subject details.
- Objects combines SAM 3 and Qwen to find and assess individual visible objects. Choose Automatic to let Qwen suggest concrete concepts, or Specific Concepts to enter comma-separated terms such as
bird, deer. SAM 3 draws a separate mask and numbered outline for each retained instance; Qwen then describes the objects and the photograph. The table shows object counts, concepts, Qwen assessment confidence, and status. Select a row, then a numbered object, to inspect its crop and detail.
The numbered overview and crops are views of one source photograph. A SAM 3 mask percentage measures the model’s confidence in that mask; Qwen assessment confidence is a separate judgment. Neither proves that an object was found or described correctly. Check each outline, crop, and description against the original photograph before making a culling decision.
All three analysis modes run locally on the Mac. See AI Step by Step for a practical Objects workflow and AI Screenshots for examples.
Semantic Search
After a catalog has been indexed with DataComp CLIP, enter a short description such as puffin, raven, or squirrel. RawCull ranks the catalog using the cached image embeddings, so later searches do not have to reprocess every image.
Search terms work best in English because DataComp CLIP was primarily trained and evaluated with English text.
Supported Models
RawCull supports DataComp CLIP for catalog-wide similarity and semantic-search indexing, plus SAM 3 and Qwen3-VL for deeper analysis of selected images.
The upstream files on Hugging Face are the source models. RawCull requires model bundles converted and validated for Apple Core AI on macOS 27.
Model Downloads
The AI models are not included in the RawCull application or its release
download. Open Settings > AI and select Download AI Models to view the
available models, their purpose, publisher, version, licence, and installation
status.
Downloads use macOS Managed Background Assets. macOS stores and manages each
asset pack, and its location can change between app launches. RawCull validates
an installed model before enabling it and falls back safely when a required
model is missing or invalid.
Review the licence in the model manager before selecting Download. Progress
and cancellation are shown in the same window. After installation, the models
run locally; photographs are not uploaded as part of downloading or using a
model.
Keeping the models separate makes the application download smaller. It does
not remove the upstream model’s licence conditions. Follow the RawCull release
notes for the model versions supported by each beta or
release rather than installing an arbitrary conversion.
Model Licences
The RawCull application licence does not replace or extend the licences for the separately downloaded models.
- The DataComp model page identifies its licence as MIT. Its model card also documents the training data, intended uses, and limitations.
- SAM 3 is distributed under Meta’s separate SAM License, not the MIT License. Access to the official Hugging Face files may require signing in, sharing the requested contact information, and accepting Meta’s terms.
- Qwen3-VL-2B-Instruct is distributed under the Apache License 2.0.
Review the complete licence and model card shown by RawCull before downloading
or using a model. RawCull records the exact model revision and licence version
applicable to each converted bundle. If a model update changes its licence,
RawCull must present the new terms before downloading that update.