Dog Breed Classification Project

This project focuses on building, training, and evaluating a deep learning model to classify different breeds of dogs based on images, there are a total of 120 breeds. The project uses TensorFlow and Keras for building the model and integrates various tools for data preprocessing, training, and evaluation.

Table of Contents

• Project Overview
• Data
• Model Architecture
• Training
• Evaluation
• Usage
• Results
• Dependencies
• How to Run
• License

Project Overview

The objective of this project is to create a machine learning model that can accurately classify dog breeds from images. The model is trained on a dataset of dog images labeled by breed and is evaluated based on its accuracy, precision, recall, and other metrics.

Data

Dataset

The dataset consists of images of dogs belonging to various breeds. The dataset is split into training and validation subsets for model training and evaluation.

• Images Directory: train/
• Labels File: labels.csv

Data Preprocessing

Before training, the images are resized to 224x224 pixels and normalized by rescaling pixel values to the range [0, 1].

Model Architecture

The model is a convolutional neural network (CNN) designed for image classification. The architecture includes:

• Convolutional layers with ReLU activation
• Max pooling layers
• Fully connected (dense) layers
• Dropout for regularization
• Softmax activation in the output layer for multi-class classification

Training

The model is trained using the categorical cross-entropy loss function and the Adam optimizer. The training process involves the following steps:

1. Data Augmentation: Applied to increase the diversity of the training data.
2. Model Compilation: Configuring the model with the loss function, optimizer, and evaluation metrics.
3. Training: Running the training process with a set number of epochs.

Evaluation

The model is evaluated on the validation dataset. The evaluation metrics include:

• Accuracy
• Confusion Matrix
• Classification Report (Precision, Recall, F1-Score)

Accuracy

The accuracy of the model on the validation set is printed during the evaluation phase.

Confusion Matrix

A confusion matrix is generated to visualize the model's performance across different classes.

Classification Report

A detailed classification report is printed, showing precision, recall, and F1-score for each class.

Usage

Training the Model

python train.py

Evaluating the Model

python evaluate.py

Dependencies

Python 3.x TensorFlow Keras NumPy Pandas Matplotlib Seaborn scikit-learn

Installing Dependencies

You can install the required dependencies using pip:

pip install -r requirements.txt