README.md

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title: Rust Auto Score Engine
emoji: 🎯
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
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# Rust Auto Score Engine

A high-performance dart scoring system architected in Rust, utilizing the Burn Deep Learning framework. This engine is the high-concurrency Rust port of the original Python-based [Dart-Vision](https://github.com/iambhabha/Dart-Vision) project.

![Dashboard Preview](image.png)

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## Live Demo (No Server Required)

The entire neural engine can run directly in your browser using **WebAssembly (WASM)**. No installation or heavy server is required.

**Try it here (WASM):** [https://iambhabha.github.io/RustAutoScoreEngine/](https://iambhabha.github.io/RustAutoScoreEngine/)
**Try it here (Hugging Face Space):** [https://huggingface.co/spaces/bhabha-kapil/RustAutoScoreEngine](https://huggingface.co/spaces/bhabha-kapil/RustAutoScoreEngine)

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## Technical Overview

The engine implements a multi-stage neural pipeline designed for millisecond-latency inference and robust coordinate recovery.

### Neural Pipeline Workflow
- **Input:** 800x800 RGB Image Frame.
- **Mapping:** YOLOv4-tiny backbone extracts point-of-interest features.
- **Verification:** Keypoint confidence filtering and objectness thresholds.
- **Symmetry Recovery:** Mathematical reconstruction of obscured calibration points.
- **Scoring:** BDO standard coordinate mapping for final point calculation.

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## CLI Reference Guide

| Command | Action | Key Features |
| :--- | :--- | :--- |
| `cargo run --release -- gui` | Start Dashboard | Web UI (8080), Live SVG Overlays, Confidence Diagnostics |
| `cargo run --release -- train` | Begin Training | Adam Optimizer, DIOU Loss, Auto-Checkpointing |
| `cargo run --release -- test <path>` | Direct Inference | Raw Coordinate Reporting, Confidence Analysis |

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## Installation and Setup

### Prerequisites
- **Rust Toolchain:** Stable channel (Latest).
- **GPU Driver:** Support for Vulkan, Metal, or DirectX 12 (via WGPU).
- **Hard Drive Space:** Minimum 1GB (excluding dataset).

### Initial Setup
1. Clone the repository.
2. Ensure `model_weights.bin` is present in the root directory.
3. For local dashboard, run the `gui` command.
4. For custom training, place images in `dataset/800/` and configuration in `dataset/labels.json`.

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## Advanced Architecture and Optimization

### 1. Distance-IOU (DIOU) Loss Implementation
Utilizing DIOU Loss ensures stable training and faster convergence for small objects like dart tips by calculating intersection over union alongside center distance.

### 2. Deep-Dart Symmetry Engine
If a calibration corner is obscured, the system invokes a symmetry-based recovery algorithm to reconstruct the board area without recalibration.

### 3. Memory & VRAM Optimization
Optimized to handle 800x800 resolution training on consumer GPUs by efficiently detaching the Autodiff computation graph during logging cycles (Usage: ~3.3GB VRAM).

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## Resources and Research

### Scientific Publications
- **arXiv Project (2105.09880):** [DeepDarts Neural Network Paper](https://arxiv.org/abs/2105.09880)
- **Original Project:** [iambhabha/Dart-Vision](https://github.com/iambhabha/Dart-Vision)

### Source Materials
- **Dataset (IEEE Dataport):** [Official DeepDarts Collection (16K+ Images)](https://ieee-dataport.org/open-access/deepdarts-dataset)
- **Framework (Burn):** [Burn Deep Learning Documentation](https://burn.dev/book/)

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*Distributed under the terms of the project's license. Built for the global darts technology ecosystem.*