This repository contains an end-to-end multi-class image classification project aimed at identifying dog breeds from images. The project leverages TensorFlow 2.x and TensorFlow Hub to build a deep learning model capable of classifying 120 different dog breeds.
The goal is to classify the breed of a dog given an image. This can be particularly useful in real-world scenarios, such as identifying the breed of a dog from a photo taken at a cafe.
The dataset used is from Kaggle's Dog Breed Identification competition. It includes:
Training Data: Approximately 10,000+ labeled images of dogs.
Test Data: Approximately 10,000+ unlabeled images for prediction.
Dataset Link: https://www.kaggle.com/c/dog-breed-identification/data
The model's performance is evaluated based on prediction probabilities for each dog breed in the test set. The evaluation metric is detailed on the Kaggle competition page.
Evaluation Link: https://www.kaggle.com/c/dog-breed-identification/overview/evaluation
Deep Learning: Utilizes TensorFlow and TensorFlow Hub for building and training the model.
Transfer Learning: Employs pre-trained models to enhance performance.
GPU Support: Ensures efficient training by leveraging GPU acceleration.
Open in Colab: The notebook is designed to run in Google Colab with GPU support.
Data Preparation: Unzip the dataset and prepare it for training.
Model Training: Run the notebook cells to import necessary libraries, prepare data, and train the model.
Evaluation: Evaluate the model's performance on the test set.
Python 3.x
TensorFlow 2.x
TensorFlow Hub
pandas