🐶 Dog Breed Classification using Transfer Learning and TensorFlow 2.0!!

This project uses TensorFlow 2.0 and transfer learning to classify different breeds of dogs. The data used comes from the Kaggle dog breed identification competition, which includes over 10,000 labeled images of 120 different dog breeds.

🚀 Getting Started

These instructions will guide you in setting up the project and running it on your local machine.

📋 Prerequisites

Before starting, you will need to have the following installed:

• Python 3.x
• TensorFlow 2.x
• TensorFlow Hub
• Kaggle (for downloading the dataset)

⬇️ Installing

1. Clone the repository

Clone this repository to your local machine. You can use the following command: git clone https://github.com/pawaspy/dog-breed-classification.git

2. Install the required Python packages

Navigate to the cloned repository and install the required packages using pip:

🏃‍♀️ Running the Project

1. Download the data

Use the Kaggle API to download the dog breed identification competition data: kaggle competitions download -c dog-breed-identification

Unzip the downloaded file and place it in a directory accessible by the script.

2. Run the script

Run the main script (e.g., main.py) in the root directory of the project: python main.py

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request