TriNet Detect: A Triple-Model Disease Detection Framework

📌 Product Requirements Document (PRD)

🧩 Project Overview

TriNet Detect is a modular deep learning framework for detecting diseases from images using three distinct architectures: CNN, YOLOv4-Tiny, and Vision Transformer (ViT). The goal is to provide accurate, architecture-specific disease classification and localization from images (e.g., plant leaves, skin conditions).

🎯 Target Users

• Agricultural researchers and farmers
• Healthcare professionals or medical students
• AI developers working on computer vision diagnostics

🔍 Key Features

• Model Selection: Run inference using CNN, YOLO, or ViT.
• Localization and Classification: Optionally highlight disease areas (YOLO) or return class predictions.
• Plug-and-Play Inference: Easy model switching via script control.
• Pretrained Models: No training required for basic usage.

✅ Success Criteria

• Achieves ≥90% accuracy on evaluation dataset
• Consistent inference speed <500ms (YOLO/CNN)
• Visual output or print logs showing detection results

🔧 Technical Documentation