CIFAR-10 Dataset Neural Network Deployment on Streamlit This repository contains code for deploying a convolutional neural network (CNN) trained on the CIFAR-10 dataset using Streamlit. Streamlit is an open-source Python library that makes it easy to create web applications for machine learning and data science projects.
Features Utilizes the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. Implements a CNN model trained on the CIFAR-10 dataset for image classification. Deploys the trained model using Streamlit to create an interactive web application. Allows users to upload their own images and get predictions from the trained model in real-time. Usage Clone this repository: bash Copy code git clone https://github.com/your-username/cifar10-dataset-neural-network-deploy-on-Streamlit.git cd cifar10-dataset-neural-network-deploy-on-Streamlit Install the required dependencies: bash Copy code pip install -r requirements.txt Run the Streamlit application: bash Copy code streamlit run app.py Once the application is running, open your web browser and go to http://localhost:8501 to access the web interface. Dependencies Python 3.x TensorFlow Streamlit NumPy Matplotlib File Structure app.py: Contains the Streamlit application code. model.py: Defines the CNN model architecture and training process. utils.py: Utility functions for data preprocessing and visualization. requirements.txt: List of Python dependencies required to run the application