https://github.com/hr-fahim/trained-deep-learning-model-deployment-from-local-storage

Web app for image classification using deep learning models; user uploads images for instant results.
https://github.com/hr-fahim/trained-deep-learning-model-deployment-from-local-storage

deep-learning project

Last synced: 1 day ago
JSON representation

Web app for image classification using deep learning models; user uploads images for instant results.

• Host: GitHub
• URL: https://github.com/hr-fahim/trained-deep-learning-model-deployment-from-local-storage
• Owner: HR-Fahim
• License: mit
• Created: 2024-06-25T03:04:16.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-06-26T03:39:39.000Z (5 months ago)
• Last Synced: 2024-06-27T04:26:08.010Z (5 months ago)
• Topics: deep-learning, project
• Language: Python
• Homepage: https://www.kaggle.com/code/habiburrahamanfahim/research-on-seti-data-95-accuracy-of-cnn-models
• Size: 1.17 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Project Overview

This project is a web-based application designed to perform image classification using a deep learning model. Users can upload an image, which the system processes and classifies into predefined categories. The application leverages a combination of state-of-the-art deep learning models to ensure high accuracy and performance.

For more details check here: [Research on Transfer Learning Architectures of CNN Models.](https://github.com/HR-Fahim/Research-on-SETI-Data-Using-CNN-Models-with-Transfer-Learning.git)

# Purpose

The purpose of this project is to provide a practical and user-friendly example of integrating advanced image classification models into a web application. It aims to serve as a template or starting point for similar projects, showcasing the deployment of machine learning models in a real-world application.

# Features

- **User-Friendly Interface**: A simple and intuitive web interface for uploading images and displaying classification results.

- **Advanced Deep Learning Models**: Utilizes powerful models like ResNet-50 and Inception-v3 for robust image classification.

- **Real-Time Predictions**: Provides instant classification results upon image upload.

- **Predefined Classes**: The application can classify images into several predefined categories (customizable based on the use case).

# Screenshots

![alt text]()

# Special Aspects

- **Model Combination**: Uses a unique combination of different deep learning models to enhance accuracy and performance.

- **Pre-trained Models**: Incorporates models that are pre-trained on large datasets, ensuring robust feature extraction and accurate predictions.

- **Seamless Deployment**: Designed for easy deployment on hosting platforms like Render, making setup and deployment straightforward.