Species Classification Model (Cat vs Dog)

This repository contains a Jupyter Notebook file that implements a deep learning model for species classification, specifically differentiating between cats and dogs. The model uses images and is built using TensorFlow and Keras.

Dataset

• The model is trained on the Dogs vs. Cats dataset from Kaggle.
• The dataset consists of a large collection of labeled images of cats and dogs.

Model Architecture

• The model is based on a convolutional neural network (CNN) architecture.
• It includes multiple convolutional layers, pooling layers, and fully connected layers.
• The final layer uses a softmax activation function to classify the input image as either a cat or a dog.

Usage

• Download the Dogs vs. Cats dataset from Kaggle.
• Extract the dataset to a local directory.
• Open the Jupyter Notebook file in this repository.
• Update the file paths in the notebook to point to the dataset directory.
• Run the notebook to train and evaluate the species classification model.

Requirements

• Python (version 3.6 or higher)
• TensorFlow (version 2.0 or higher)
• Keras (version 2.0 or higher)
• Jupyter Notebook

Results

• The model's performance can be evaluated using metrics such as accuracy, precision, recall, and F1-score.
• The results can be viewed and analyzed within the Jupyter Notebook.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• The Dogs vs. Cats dataset is provided by Kaggle.
• The model architecture and implementation are inspired by various resources and tutorials in the field of deep learning.

For more details and a step-by-step guide, refer to the Jupyter Notebook file in this repository.