Dog Breed Identification

This project focuses on identifying the breed of a dog given an image using deep learning and transfer learning techniques.

Table of Contents

• Problem Statement
• Data
• Evaluation
• Features
• Setup
• Usage
• Model Training
• Results
• Contributing
• License

Problem Statement

The primary goal of this project is to identify the breed of a dog from a given image. This is a multi-class classification problem where the task is to classify images into one of many possible dog breeds.

Data

The dataset used for this project is sourced from Kaggle's Dog Breed Identification competition. It includes:

• Over 10,000 images in the training set, each labeled with a dog breed.
• Over 10,000 images in the test set without labels.

Evaluation

The model's performance is evaluated based on the prediction probabilities for each dog breed for each test image.

Features

• The project uses images (unstructured data), making deep learning and transfer learning appropriate techniques.
• Over 10,000 labeled images in the training set.
• Over 10,000 unlabeled images in the test set.

Setup

To set up the project environment, ensure you have the following dependencies installed:

• TensorFlow
• TensorFlow Hub
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

pip install tensorflow tensorflow-hub pandas numpy matplotlib scikit-learn

Additionally, ensure you have access to a GPU for faster training.

Usage

1. Clone the repository.
2. Ensure you have the dataset downloaded from Kaggle and placed in the appropriate directory.

To preprocess the images and prepare the data for training:

import pandas as pd

# Load labels
labels_csv = pd.read_csv("path/to/labels.csv")

# Preprocess images and labels
# Your preprocessing code here

Model Training

To train the model, follow these steps:

1. Import necessary libraries and ensure the GPU is available.
2. Load and preprocess the data.
3. Split the data into training and validation sets.
4. Define the model architecture using TensorFlow and TensorFlow Hub.
5. Compile and train the model.

Example code snippet to check for GPU availability:

import tensorflow as tf

print("GPU", "available" if tf.config.list_physical_devices("GPU") else "not available")

Example code snippet for model training:

# Define model architecture
model = tf.keras.Sequential([
    # Your model layers here
])

# Compile the model
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Train the model
history = model.fit(
    # Your training data here
)

Results

The results will be a set of prediction probabilities for each dog breed for each test image. The detailed evaluation metrics and performance charts will be added here after running the model.

License

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

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This README provides a structured and professional overview of the Dog Breed Identification project, making it easier for others to understand and contribute.