---
title: CIFAR-10 Vision AI
emoji: 👁️
colorFrom: indigo
colorTo: purple
sdk: docker
pinned: false
---

# CIFAR-10 CNN Classifier

This project implements a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset into 10 categories.

## Dataset
The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.

## Model Architecture
The CNN uses a multi-block architecture:
- 3 Convolutional Blocks:
  - 2x Conv2D layers with ReLU activation
  - Batch Normalization
  - Max Pooling
  - Dropout for regularization
- Flattened layer
- Dense hidden layer (128 units)
- Output layer (10 units with Softmax)

## Setup and Usage
1. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
2. Run the training script:
   ```bash
   python train_cifar10.py
   ```

## Files
- `train_cifar10.py`: The main training and evaluation script.
- `web_app/`: Complete web application for interactive inference.
  - `server.py`: Flask backend.
  - `static/`, `templates/`: Frontend assets.
- `implementation_plan.md`: Detailed plan of the implementation.
- `requirements.txt`: Python package dependencies.

## Web Application
To run the interactive vision tool:
1. Navigate to the web app directory:
   ```bash
   cd web_app
   ```
2. Start the server:
   ```bash
   python server.py
   ```
3. Open `http://127.0.0.1:5000` in your browser.