Dog Breed Identification with Neural Networks

This project focuses on using artificial intelligence and neural networks to create a model capable of identifying dog breeds from images. The project leverages Google Colab and TensorFlow for model training and prediction.

Project Overview

The primary objective of this project is to develop a prediction model that can accurately classify dog breeds based on images. The dataset used for training consists of over 10,000 images and is sourced from the Kaggle Dog Breed Identification competition.

Dataset

The dataset used for training and testing the model is obtained from the Kaggle Dog Breed Identification competition.

Files

• Model and Code: The project files and the trained model can be accessed from this Google Drive link.

Dependencies

Ensure you have the following dependencies installed:

• Python 3.x
• TensorFlow
• NumPy
• Pandas
• Matplotlib
• OpenCV

You can install these dependencies using pip:

pip install tensorflow numpy pandas matplotlib opencv-python

Instructions

Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/your-username/dog-breed-classification.git
cd dog-breed-classification

Model Training

Data Preprocessing

The images are loaded and preprocessed, including resizing and normalization, to prepare them for training.

Model Architecture

The Convolutional Neural Network (CNN) architecture is defined, consisting of:

• Convolutional layers
• Pooling layers
• Fully connected layers

Training

The model is trained on the dataset using appropriate:

• Loss functions
• Optimizers

Evaluation

The model's performance is evaluated on the validation set, and hyperparameters are adjusted as needed to optimize results.

Results

The trained model achieves a high accuracy in classifying dog breeds. Detailed results, including accuracy and loss plots, can be found in the Jupyter Notebook.