Show HN: Image Categorizer – sort a photo library with local vision models

A developer released Image Categorizer, an open-source tool that sorts a photo library into categories using local vision and text models served by Ollama, keeping all photos on the user's own network. The tool describes each photo with a vision model (qwen3.6:35b) and flags accidental shots, then sorts photos into user-editable categories with a text model (mistral-small3.2:24b), exporting a bash script that moves files into folders or writes the category into each photo's caption. The developer reports that 1,195 photos took about 80 minutes to describe and 8 minutes to sort on an RTX 4090, and the tool never moves, renames or deletes a photo itself.

Sorts a folder of photos into categories using local AI models served by Ollama https://ollama.com . A vision model describes every photo and flags accidental shots; a text model sorts the photos into categories you keep and refine across runs. You review the result in a browser report and export a script that moves the files into folders, writes each category into the photo's caption, or both. No photo leaves your network. Built for camera rolls full of accidental shots, near-duplicates and screenshots, where reviewing every photo by hand is not practical. 1. Describe. A vision model looks at each photo, writes a short description, and decides whether it is an accidental or failed shot motion blur, pocket shot, no subject . With --describe-only see below , descriptions are saved as they go and an interrupted run resumes where it stopped. 2. Sort. A text model assigns every photo to a category using the descriptions only, so re-sorting takes minutes and never looks at the images again. Each category has a rule describing what belongs in it, and the list is saved and reused for every library. 3. Review. A report in the browser lets you move photos between categories, trash them, and rename, merge or create categories. 4. Apply. The report exports a bash script: move each photo into a folder named after its category, write Category: <name into its caption searchable in the Photos app on Mac and iPhone , or both. Nothing is deleted. The tool itself never moves, renames or deletes a photo. With an AI coding agent. Open the repository in Claude Code or a similar agent and ask it to follow SETUP.md https://github.com/DrBenedictPorkins/image-categorizer/blob/main/SETUP.md . It checks the machine, installs what is missing, configures Ollama and the models, and runs a smoke test on the images in samples/ https://github.com/DrBenedictPorkins/image-categorizer/blob/main/samples . By hand. You need uv https://docs.astral.sh/uv/ and an Ollama server, on this machine or another one on your network. git clone https://github.com/DrBenedictPorkins/image-categorizer.git cd image-categorizer uv sync ollama pull qwen3.6:35b vision model describe ollama pull mistral-small3.2:24b text model sort cp .env.example .env set OLLAMA HOST if Ollama runs elsewhere uv run python main.py samples --provider ollama The last command sorts the eleven sample images and opens the report. Smaller model pairs for 8-16 GB GPUs are listed in SETUP.md https://github.com/DrBenedictPorkins/image-categorizer/blob/main/SETUP.md 5-ollama-and-models . Describe once, then sort as often as you like: Describe every photo hours for thousands; rerun the same command to resume uv run python main.py "/path/to/photos" --description-provider ollama --describe-only Optional: build the category list and stop, to edit it before sorting uv run python main.py "/path/to/photos" --categorization-provider ollama \ --categorize-from "/path/to/photos/descriptions only.json" --plan-categories --max-categories 12 Sort and open the report minutes uv run python main.py "/path/to/photos" --categorization-provider ollama \ --categorize-from "/path/to/photos/descriptions only.json" --max-categories 12 For reference: 1,195 photos took about 80 minutes to describe and 8 minutes to sort with qwen3.6:35b and mistral-small3.2:24b on an RTX 4090. - Photos grouped by category; the rail on the left shows counts and accepts drops - Move a photo by dragging it or with its category menu, which also offers the model's suggested categories - Rename, merge rename to an existing name , create and remove categories; edit each category's rule - Trash with restore; move a whole category to Trash in one step - Full-size preview with the model's description; arrow keys browse the category - Save categories downloads the category list, with your edits, for future runs - Export script writes the organize script Writing captions requires exiftool https://exiftool.org . An existing caption is kept; a previous Category: part is replaced, so the script can be run again. Categories are saved in ~/.config/image-categorizer/categories.yaml override with CATEGORIES FILE and reused for every library: categories: - name: Portraits rule: Photos of people looking at the camera, including selfies. Not group photos. status: kept The first run builds the list from the photos. Later runs sort into the saved list; only when enough photos fit none of the rules does the model propose a new category, marked new in the report until you save the list. --max-categories caps the list: once it is full, photos that fit nothing go to Unsorted . When a category holds photos that belong elsewhere, click the re-sort button on that category and describe what went wrong. uv run python main.py --resort ~/Downloads/resort.json The text model turns the note into rule changes and asks you to approve them, shows how 10 sample photos would move, then re-sorts only that category. Photos that fit no other category stay put. Moved photos are marked Re-sorted in the rebuilt report, and edits made in the report before exporting are kept. Settings live in .env ; .env.example https://github.com/DrBenedictPorkins/image-categorizer/blob/main/.env.example lists them. | Variable | Purpose | |---|---| | OLLAMA HOST | Ollama server, default http://localhost:11434 | | OLLAMA MODEL | Vision model for describing | | OLLAMA TEXT MODEL | Text model for sorting and re-sorting | | CATEGORIES FILE | Saved category list | | MAX CATEGORIES | Category cap | All command-line options: uv run python main.py --help and SETUP.md https://github.com/DrBenedictPorkins/image-categorizer/blob/main/SETUP.md 12-reference . Supported formats: .jpg , .jpeg , .png , .gif , .bmp , .webp , .heic , .heif ; videos are skipped. The report shows HEIC photos through JPEG previews kept in a hidden .image-categorizer-previews folder. --provider huggingface runs BLIP-2, LLaVA or Flan-T5 in-process without Ollama docs/HUGGINGFACE PROVIDER.md https://github.com/DrBenedictPorkins/image-categorizer/blob/main/docs/HUGGINGFACE PROVIDER.md , and --categorization-provider keyword sorts by keyword matching. Both predate the saved categories and re-sort features, and are less maintained. | Path | Contents | |---|---| | main.py | Command line, workflows, re-sort | | providers/ollama provider.py | Describing, sorting, category proposals, rule rewrites | | core/categories.py | Saved category list | | core/html generator.py , template.html | Report | | models/image data.py | Result data model | | SETUP.md | Install and first run, written for AI coding agents | | samples/ | Public-domain sample images sources https://github.com/DrBenedictPorkins/image-categorizer/blob/main/samples/README.md | | docs/ | Provider guides, dependency audit, design notes, README images | | sundry/ | Archive of one-off scripts, not used by the app | MIT. See LICENSE https://github.com/DrBenedictPorkins/image-categorizer/blob/main/LICENSE .