| ---
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| title: Project Halide
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| sdk: gradio
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| sdk_version: 6.10.0
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| app_file: app.py
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| license: apache-2.0
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| models:
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| - Lonelyguyse1/halide-vision
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| - openbmb/MiniCPM-V-4.6
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| - nvidia/Nemotron-Mini-4B-Instruct
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| tags:
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| - gradio
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| - film
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| - computer-vision
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| - diagnostics
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| - track:backyard
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| - sponsor:openbmb
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| - sponsor:nvidia
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| - sponsor:modal
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| - sponsor:openai
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| - badge:off-brand
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| - badge:offbrand
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| - badge:tiny-titan
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| - badge:tiny
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| - badge:best-demo
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| - badge:demo
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| - badge:best-agent
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| - badge:bonus-quest
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| - badge:quest-champion
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| - badge:quest
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| - achievement:offgrid
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| - achievement:welltuned
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| - achievement:offbrand
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| - achievement:fieldnotes
|
| ---
|
|
|
| # Project Halide
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|
|
| Project Halide is an edge-native diagnostic workbench for analog film scans by
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| [Lonelyguyse1](https://huggingface.co/Lonelyguyse1).
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|
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| The runtime uses MiniCPM-V 4.6 for defect extraction and
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| Nemotron-Mini-4B-Instruct for diagnostic reasoning. The vision pass combines
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| full-frame inspection, tiled fallback for large scans, a conservative
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| image-analysis validator for obvious scratches, and geometric filtering for
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| sprocket or frame-edge artifacts. Model inference runs on the Space GPU runtime
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| without cloud inference APIs.
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|
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| Fine-tuned vision model:
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| <https://huggingface.co/Lonelyguyse1/halide-vision>
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|
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| Fine-tuning improved the vision stage where it mattered most for the app:
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| structured defect JSON, consistent film-defect labels, scratch and
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| emulsion-damage vocabulary, and fewer obvious false positives on clean or
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| lookalike regions. The runtime still treats model output as candidate evidence
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| and validates every box.
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|
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| The data bottleneck was central to the build. Public damaged-film examples are
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| scattered, noisy, and often not real negatives, so the training curriculum
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| combines FilmDamageSimulator annotations, procedural defect positives, synthetic
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| scratches and stains, hard clean negatives, and lookalike counterexamples such
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| as grass, subject hair, sprocket holes, borders, and glare. The five private
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| negatives stayed held out for evaluation only.
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|
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| Source repository:
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| <https://github.com/LonelyGuy-SE1/Project-Halide>
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|
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| Demo video:
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| <https://youtube.com/watch?si=apzCiBZcIZWC1nFt&v=DGJ2M1aQCrE&feature=youtu.be>
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|
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| Public launch post:
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| <https://x.com/lonelyguyse1/status/2066631507956105423?s=20>
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|
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| Technical blog:
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| <https://lonelyguy.vercel.app/articles/2026-06-16-project-halide>
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|
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| Modal was used for offline training, held-out GPU evaluation, checkpoint upload,
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| GGUF conversion, and Space deployment. The runtime app itself does not call
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| Modal or any hosted inference API.
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|
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| ## How It Works
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|
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| 1. Upload a film scan, negative photo, or contact-sheet crop.
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| 2. MiniCPM-V 4.6 extracts candidate defects as structured JSON.
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| 3. The validator normalizes boxes, filters bad geometry, removes duplicate or
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| sprocket-like edge artifacts, and adds high-precision scratch candidates
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| when clear linear evidence is visible.
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| 4. Nemotron-Mini-4B-Instruct reads the validated evidence plus user metadata and
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| writes a lab-style diagnosis with physical fixes.
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| 5. SQLite stores local diagnostic history so earlier runs can be reopened.
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|
|
| ## Sponsor Usage
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|
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| - OpenBMB: MiniCPM-V 4.6 is the primary vision model, fine-tuned for film defect
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| extraction and published at `Lonelyguyse1/halide-vision`.
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| - NVIDIA: Nemotron-Mini-4B-Instruct produces the diagnostic report and keeps
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| uncertain film metadata lower priority than visible evidence.
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| - Modal: used offline for training, evaluation, checkpoint export, GGUF
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| conversion, model upload, and Space deployment support.
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| - OpenAI: assisted implementation, review, and source-control hygiene through
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| the linked repository workflow.
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|
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| ## Field Guide Alignment
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|
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| - Gradio Space under the official `build-small-hackathon` organization.
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| - All runtime inference uses open weights on the Space GPU, with no hosted model
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| API calls.
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| - Model sizes stay under the 32B limit, with MiniCPM-V 4.6 at 1.3B parameters
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| and Nemotron-Mini-4B-Instruct at 4B parameters.
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| - Custom autumn-themed UI with a purpose-built compare viewer and diagnostic
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| history.
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| - Fine-tuned vision model and GGUF artifact are published on the author's
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| Hugging Face profile.
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| - Demo video, technical blog, public launch post, and field notes are linked
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| from this Space.
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|
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| Held-out validation summary:
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|
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| - Four visibly damaged private negatives were detected with scratch and
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| emulsion-damage evidence.
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| - One near-clean private negative returned zero defects.
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| - A broad lifted crack network that failed full-frame inference was recovered by
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| the tiled fallback.
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|
|
