CNNVision

cnn image recognition

CNN image recognition project

Directory Structure:

.
cnn-image-classifier/
├── data/
│   ├── raw/                # Unprocessed images
│   ├── processed/          # Preprocessed images (resized, normalized)
├── models/
│   └── cnn_model.pth       # Saved PyTorch model
├── notebooks/
│   └── exploration.ipynb   # EDA and prototyping
├── src/
│   ├── __init__.py
│   ├── config.py           # Hyperparameters and paths
│   ├── dataset.py          # Dataset loading and transforms
│   ├── model.py            # CNN architecture
│   ├── train.py            # Training loop
│   ├── evaluate.py         # Accuracy, confusion matrix
│   ├── predict.py          # Inference on new images
├── tests/
│   └── test_model.py       # Unit tests
├── requirements.txt
├── README.md
└── run.py                  # CLI entry point

README.md:

# CNN Image Recognition

Convolutional Neural Network (CNN) image recognition project for classifying images.

## Overview

This project uses a CNN model to perform image recognition. It includes modules for training, testing, and using the pre-trained model for predictions.

## Table of Contents

- [Demo](#demo)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Training](#training)
- [Testing](#testing)
- [Contributing](#contributing)
- [License](#license)

## Demo

Include a link or GIF demonstrating your CNN image recognition system in action.

## Prerequisites

- Python 3
- Dependencies listed in `requirements.txt`

## Installation

```bash
pip install -r requirements.txt

Usage

from image_recognition import CNNModel

model = CNNModel()
result = model.predict(image_path)
print(result)

Training

python train.py --dataset_path /path/to/dataset --epochs 10

Testing

python test.py

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

License

This project is licensed under the MIT License - see the LICENSE file for details.

### LICENSE:

Create a file named `LICENSE` and add the MIT License text:

MIT License

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