Pre-trained Image Classifier to Identify Dog Breeds

Project Overview

This project utilizes a pre-trained image classifier to identify different dog breeds. The model is based on TensorFlow and Keras and is fine-tuned using a MobileNet architecture. The classifier is trained on a large dataset of dog breed images and is capable of predicting the breed of any input dog image.

Features

• Pre-trained Model: The model uses MobileNet, a popular deep learning architecture that has been pre-trained on the ImageNet dataset, for fast and accurate image classification.
• Breed Prediction: The model classifies an input image and provides the predicted dog breed.
• Top-K Predictions: The model can predict multiple dog breeds, providing the top K breed predictions with associated probabilities.
• Model Saving and Loading: The model is saved after training and can be loaded for future predictions.

Technologies Used

• Python for implementing the image classification model.
• TensorFlow and Keras for building and training the neural network.
• OpenCV for image processing (optional, depending on the implementation).
• NumPy and Matplotlib for data manipulation and visualization.

Dataset

The model is trained on the Stanford Dogs Dataset, which includes 120 different dog breeds and over 20,000 images.

• Dataset link: Stanford Dogs Dataset

Setup and Installation

Prerequisites

Make sure you have the following software installed:

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

Steps to Run the Project

1. Clone the repository: Clone the repository by running:

   git clone https://github.com/your-username/Use-a-Pre-trained-Image-Classifier-to-Identify-Dog-Breeds-master.git