🧠 Image Classification Using CNN

A Gradio-powered deep learning web app that classifies images as Dog, Cat, or Person using Convolutional Neural Networks.
Built with ❤️ using Python, PyTorch, OpenCV, and Gradio. Hosted on Hugging Face Spaces.

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📌 Table of Contents

• 📖 About the Project
• 🧰 Tech Stack
• 🚀 Getting Started
• 💻 Usage
• 📸 Screenshots / Demo
• 🤝 Contributing
• 📜 License
• 📬 Contact

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📖 About the Project

This is an Image Classification Web Application built using Convolutional Neural Networks (CNN) to identify whether an image is of a Dog, Cat, or a Person.

• The model was trained on a custom dataset curated by combining two Kaggle datasets:
  • Dogs vs. Cats
  • Man vs. Woman
• Each class (Dog, Cat, Person) was created using 50 training and 50 testing images, giving a balanced dataset.
• The model achieved a training accuracy of 99.63%.
• The application features a sleek Gradio interface, and is hosted on Hugging Face Spaces for real-time predictions and demonstrations.

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🧰 Tech Stack

• Languages & Libraries:
  • Python
  • PyTorch
  • Scikit-learn
  • PIL (Python Imaging Library)
  • NumPy
  • OpenCV
  • Gradio (for UI)

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🚀 Getting Started

To run this project locally on your system, follow these steps:

1. Clone the Repository

git clone https://github.com/shashibhushan01/image-classification-cnn.git
cd image-classification-cnn

### 2. Install Required Dependencies
Make sure Python 3.x is installed. Then run:- **pip install -r requirements.txt**

## 💻 Usage

--> Open the Gradio interface in your browser.

--> Upload an image of a dog, cat, or a person.

--> The CNN model will classify and display the result with confidence scores