Git Product home page Git Product logo

mobile-pest-identification's Introduction

A Lightweight Deep Learning Model for Automated Pest Detection on Mobile.

User Guide

Step 1. To clone this repository, run:
git clone <this repo>
Step 2. To install dependencies run:
pip install -r requirements.txt
Step 3. Download pest-imagery IP102v1.1 dataset:
Note: This dataset contains more than 75,000 images belonging to 102 categories, only a subset of which is showcased in this repository.

Step 4. To run the model locally, run:
python src/pest.py

This will load the data, train the model and save the mobile optimized version in src/model/pest/. For deploying the model to a mobile app, see PyTorch mobile.

Demonstration on the Wikilimo App:

mobile-pest-identification's People

Contributors

roshni-b avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.