Implementation-of-ML-model-for-image-classification Implementation-of-ML-model-for-image-classification is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, which is ideal for learning and practical use.
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: Seamlessly 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. Getting Started Prerequisites Python 3.7 or later A web browser Installation Clone the repository:
git clone https://github.com/JayRathod341997/DeepLensX.git cd Implementation-of-ML-model-for-image-classification Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
Install the required packages:
pip install -r requirements.txt
Start the Streamlit app:
streamlit run app.py Acknowledgements Streamlit TensorFlow