🐶🐱 Dog vs Cat Classification

This project implements an image classification model to distinguish between dogs and cats using deep learning techniques. The model is trained on labeled image data and evaluated for accuracy and performance.

📌 Project Overview

• Binary image classification: Dog vs Cat
• Implemented using a Convolutional Neural Network (CNN)
• Developed in a Jupyter Notebook (dogcat.ipynb)
• Focus on training, validation, and evaluation of the model

🧠 Model Details

• Model Type: Convolutional Neural Network (CNN)
• Framework: (Specify — e.g., TensorFlow / PyTorch / Keras)
• Loss Function: Binary Crossentropy
• Optimizer: Adam (or specify if different)
• Metrics: Accuracy

📂 Project Structure

cat-dog-classifier/
├── dogcat.ipynb                # Main Jupyter notebook
├── cat_dog_classifier.pth      # Saved model weights (after training)
├── predictions.csv             # Output of batch predictions
└── README.md

🚀 How to Run

1. Clone the repository:

git clone <your-repo-link>
cd <repo-folder>

2. Install dependencies:

pip install -r requirements.txt

3. Launch Jupyter Notebook:

jupyter notebook

4. Open:

dogcat.ipynb

5. Run all cells step-by-step.

📊 Dataset

• Dataset consists of labeled images of dogs and cats
• Images are preprocessed (resized, normalized, etc.)
• Dataset source: https://www.kaggle.com/c/dogs-vs-cats/data

⚙️ Features

• Data preprocessing and augmentation
• Model training and validation
• Performance visualization (loss & accuracy plots)
• Prediction on new images

📈 Results

• Training Accuracy: XX%
• Validation Accuracy: XX%
• (Update with your actual results)

🛠️ Requirements

numpy
pandas
matplotlib
tensorflow / torch
scikit-learn
opencv-python

📌 Future Improvements

• Hyperparameter tuning
• Use transfer learning (e.g., ResNet, VGG)
• Improve dataset size and quality
• Deploy as a web application

🤝 Contributing

Feel free to fork this repository and submit pull requests.

📄 License

This project is open-source and available under the MIT License.