https://github.com/mktechai-0786/imagexclassify

Image Classification using CNN Model
https://github.com/mktechai-0786/imagexclassify

Last synced: 16 days ago
JSON representation

Image Classification using CNN Model

• Host: GitHub
• URL: https://github.com/mktechai-0786/imagexclassify
• Owner: MKTechAI-0786
• Created: 2024-12-09T18:04:16.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-10T09:36:53.000Z (about 1 month ago)
• Last Synced: 2024-12-10T10:26:32.965Z (about 1 month ago)
• Language: Python
• Size: 17.8 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# ImageXclassY

Image Classification using CNN Model 

This is a innovative Streamlit application seamlessly integrates the powerful MobileNetV2 and CIFAR-10 models for advanced image classification. Users can effortlessly upload their images and receive detailed predictions along with confidence scores from either model. The app boasts a sleek and intuitive navigation bar, allowing for easy switching between models and real-time results. This makes it an ideal tool for both learning and practical applications, providing a user-friendly experience for all.

## Key Features

- **Dual Model Support**:

  - **MobileNetV2 (ImageNet)**: Recognizes 1,000 different classes from the ImageNet dataset, including everyday objects, animals, and vehicles.

  - **Custom CIFAR-10 Model**: Specializes in classifying images into one of ten specific categories such as airplanes, automobiles, and birds.

- **Intuitive Interface**:

  - **Navigation Bar**: TO switch between MobileNetV2 and CIFAR-10 models using a sleek sidebar menu.

  - **Real-Time Classification**: Upload an image to receive immediate predictions with confidence scores.

- **Educational and Practical Use**:

  - Ideal for learning about deep learning models and their performance.

  - Useful for practical applications where image classification is needed for better performance.

## Getting Started ###

### Prerequisites

- Python 3.7 or later

- A web browser (Google Chrome, Mozilla Firefox, or equivalent to access the Streamlit interface.)

### Installation steps

1. **Create and activate a virtual environment**:

    #open cmd promot where the source code is saved

    python -m venv venv

    venv\Scripts\activate  #for Windows

2. **Install the required packages**:

    pip install -r requirements.txt

3. **Start the Streamlit app**:

    streamlit run app.py

4. **Open the app**: 

    The app will open in your default web browser. If not, navigate to http://localhost:8501

### Usage

  1. Use the navigation bar to select either the MobileNetV2 or CIFAR-10 model.

  2. Upload an image file (JPG or PNG or JPEG) FORMAT.

  3. View the classification results and confidence scores WITH ACCURACY VALUE.

### Acknowledgements

  - Streamlit

  - TensorFlow