Skip to content

rathoddhruv/PlantPilotAI-Fullstack

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

42 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PlantPilotAI - Active Learning Platform

An intelligent plant disease detection system using YOLOv8 and active learning.

🚀 Quick Start

Windows

Double-click start_dev.bat or run:

start_dev.bat

Linux/Mac

chmod +x start_dev.sh
./start_dev.sh

Manual (Recommended for Development)

See QUICKSTART.md for detailed instructions.

📖 Documentation

  • Quick Start Guide - How to run the project
  • Walkthrough - Development history and features

🌐 Access Points

🎯 Features

  • Drag & drop ZIP upload for Label Studio exports
  • Real-time training with GPU acceleration (CUDA)
  • Live training logs streaming to UI
  • Resizable layout with splitters
  • System dashboard with run history
  • Model rollback capability
  • Fresh start / append mode for datasets

🛠️ Tech Stack

  • Backend: FastAPI, PyTorch, YOLOv8, Ultralytics
  • Frontend: Angular 17 (Standalone), TypeScript, SCSS
  • ML: Active Learning, Object Detection

📝 Requirements

  • Python 3.11
  • Node.js 18+
  • NVIDIA GPU with CUDA support (optional, falls back to CPU)

About

Project name: PlantPilotAI-Fullstack Description: Fullstack implementation of PlantPilotAI with a FastAPI backend, integrated YOLO-based active learning pipeline, and optional frontend for managing predictions, model training, and data review.

Resources

License

MIT, MIT licenses found

Licenses found

MIT
LICENSE
MIT
LICENSE.md

Stars

Watchers

Forks

Packages

 
 
 

Contributors