Image Classification: Animals (Cats, Dogs, and Foxes)

A deep learning project to classify animal images using PyTorch and Keras. This project uses a dataset of Cats, Dogs, and Foxes to train a Convolutional Neural Network (CNN).

🚀 Features

• Dataset Integration: Automatically downloads the dataset from Kaggle using kagglehub.
• Model Architecture: Custom CNN implementation in PyTorch.
• Preprocessing: Robust image preprocessing and augmentation.
• Inference: Prediction script for individual images.

🛠️ Tech Stack

• Languages: Python
• Libraries:
  • PyTorch
  • TensorFlow / Keras (for inference)
  • Torchvision
  • Matplotlib
  • NumPy

📂 Project Structure

• Image_Classification.ipynb: Main notebook containing data loading, training, and evaluation logic.
• animal_classifier.pth: Trained model weights (generated after training).

📊 Dataset

The project uses the Animal Image Dataset (Cats, Dogs, and Foxes) from Kaggle.

📝 Usage

1. Open the .ipynb file in Google Colab or a local Jupyter environment.
2. Ensure you have the required dependencies installed:

   pip install torch torchvision tensorflow kagglehub matplotlib numpy

3. Run the cells sequentially to download the data and train the model.

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Created by VastSea0