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Cat vs Dog Classification is a DL project that uses a Convolutional Neural Network (CNN) to classify images of cats and dogs. This project involves training a CNN model on labeled image data, enabling accurate prediction of whether an image contains a cat or a dog. Ideal for learning & applying image classification techniques using neural network.

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Hafeez-UrRehman/Cat_Vs_Dog_using_CNN

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Cat vs Dog Classification using CNN

Welcome to the "Cat vs Dog" classification project! 🐱🐶

Overview

This project focuses on building a Convolutional Neural Network (CNN) to classify images of cats and dogs. The dataset consists of labeled images of both cats and dogs, and the model is designed to accurately distinguish between the two.

Dataset

The dataset URL is present in the code because of its huge size I haven't added that dataset in the repositary. The dataset is spilted in train, test and validation.

Tools and libraries

  1. Python 3.0
  2. TensorFlow/Keras
  3. Google Colab
  4. Pandas

Features

  1. CNN Architecture: Implements a Convolutional Neural Network for effective image feature extraction and classification.
  2. Data Handling: Utilizes a dataset of cat and dog images, preprocessed and augmented for improved model performance.
  3. Model Training: Includes training scripts with hyperparameter tuning to optimize model accuracy.

Contributing

Feel free to contribute to this project! Please fork the repository and submit a pull request with your changes.

Acknowledgments

The dataset used in this project is sourced from Kaggle. Special thanks to the TensorFlow and Keras communities for their support and resources.

About

Cat vs Dog Classification is a DL project that uses a Convolutional Neural Network (CNN) to classify images of cats and dogs. This project involves training a CNN model on labeled image data, enabling accurate prediction of whether an image contains a cat or a dog. Ideal for learning & applying image classification techniques using neural network.

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