README.md

metadata

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

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 project.

---

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/ Try it here (Hugging Face Space): https://huggingface.co/spaces/bhabha-kapil/RustAutoScoreEngine

---

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.

---

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

---

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.

---

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).

---

Resources and Research

Scientific Publications

• arXiv Project (2105.09880): DeepDarts Neural Network Paper
• Original Project: iambhabha/Dart-Vision

Source Materials

• Dataset (IEEE Dataport): Official DeepDarts Collection (16K+ Images)
• Framework (Burn): Burn Deep Learning Documentation

---

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