---
title: Project Halide
sdk: gradio
sdk_version: 6.10.0
app_file: app.py
license: apache-2.0
models:
  - Lonelyguyse1/halide-vision
  - openbmb/MiniCPM-V-4.6
  - nvidia/Nemotron-Mini-4B-Instruct
tags:
  - gradio
  - film
  - computer-vision
  - diagnostics
  - track:backyard
  - sponsor:openbmb
  - sponsor:nvidia
  - sponsor:modal
  - sponsor:openai
  - badge:off-brand
  - badge:offbrand
  - badge:tiny-titan
  - badge:tiny
  - badge:best-demo
  - badge:demo
  - badge:best-agent
  - badge:bonus-quest
  - badge:quest-champion
  - badge:quest
  - achievement:offgrid
  - achievement:welltuned
  - achievement:offbrand
  - achievement:fieldnotes
---

# Project Halide

Project Halide is an edge-native diagnostic workbench for analog film scans by
[Lonelyguyse1](https://huggingface.co/Lonelyguyse1).

The runtime uses MiniCPM-V 4.6 for defect extraction and
Nemotron-Mini-4B-Instruct for diagnostic reasoning. The vision pass combines
full-frame inspection, tiled fallback for large scans, a conservative
image-analysis validator for obvious scratches, and geometric filtering for
sprocket or frame-edge artifacts. Model inference runs on the Space GPU runtime
without cloud inference APIs.

Fine-tuned vision model:
<https://huggingface.co/Lonelyguyse1/halide-vision>

Source repository:
<https://github.com/LonelyGuy-SE1/Project-Halide>

Demo video:
<https://huggingface.co/spaces/build-small-hackathon/project-halide/blob/main/assets/demo_walkthrough.mp4>

Public launch post:
<https://huggingface.co/spaces/build-small-hackathon/project-halide/discussions/1>

Technical blog:
<https://lonelyguy.vercel.app/articles/2026-06-16-project-halide>

Modal was used for offline training, held-out GPU evaluation, checkpoint upload,
GGUF conversion, and Space deployment. The runtime app itself does not call
Modal or any hosted inference API.

## How It Works

1. Upload a film scan, negative photo, or contact-sheet crop.
2. MiniCPM-V 4.6 extracts candidate defects as structured JSON.
3. The validator normalizes boxes, filters bad geometry, removes duplicate or
   sprocket-like edge artifacts, and adds high-precision scratch candidates
   when clear linear evidence is visible.
4. Nemotron-Mini-4B-Instruct reads the validated evidence plus user metadata and
   writes a lab-style diagnosis with physical fixes.
5. SQLite stores local diagnostic history so earlier runs can be reopened.

## Sponsor Usage

- OpenBMB: MiniCPM-V 4.6 is the primary vision model, fine-tuned for film defect
  extraction and published at `Lonelyguyse1/halide-vision`.
- NVIDIA: Nemotron-Mini-4B-Instruct produces the diagnostic report and keeps
  uncertain film metadata lower priority than visible evidence.
- Modal: used offline for training, evaluation, checkpoint export, GGUF
  conversion, model upload, and Space deployment support.
- OpenAI: source-control history includes the required attributed development
  work in the linked GitHub repository.

## Field Guide Alignment

- Gradio Space under the official `build-small-hackathon` organization.
- All runtime inference uses open weights on the Space GPU, with no hosted model
  API calls.
- Model sizes stay under the 32B limit, with MiniCPM-V 4.6 at 1.3B parameters
  and Nemotron-Mini-4B-Instruct at 4B parameters.
- Custom autumn-themed UI with a purpose-built compare viewer and diagnostic
  history.
- Fine-tuned vision model and GGUF artifact are published on the author's
  Hugging Face profile.
- Demo video, technical blog, public launch post, and field notes are linked
  from this Space.

Held-out validation summary:

- Four visibly damaged private negatives were detected with scratch and
  emulsion-damage evidence.
- One near-clean private negative returned zero defects.
- A broad lifted crack network that failed full-frame inference was recovered by
  the tiled fallback.