Dog Breed Identification 🐕📷

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Overview

The Dog Breed Identification project integrates Django with Convolutional Neural Networks (CNNs) to develop a web application that can accurately predict a dog's breed from an uploaded image. This tool aims to assist pet owners, veterinarians, shelters, and dog buyers in identifying dog breeds quickly and accurately.

Watch the demo video here: [https://www.linkedin.com/posts/tobiloba-oluwadamilare-a850b0223_convolutionalneuralnetworks-tensorflow-django-ugcPost-7237024131546828800-9wrR?utm_source=share&utm_medium=member_desktop]

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Project Goals

• Accurate Breed Prediction: A deep learning model that can predict the breed of a dog from an image, trained on a dataset containing 8 different dog breeds.
• Buyer Verification Tool: Helps dog buyers verify if a dog's breed matches a seller's claim, aiding in informed purchasing decisions.
• User-Friendly Interface: Built with Django, the web app provides an intuitive platform for users to upload images and receive predictions in real-time.
• Seamless Integration: The CNN model is fully integrated into the Django application, providing immediate predictions with a smooth user experience.

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Methodology

1. Data Collection & Preprocessing:

   • A dataset of images from 8 distinct dog breeds was collected.
   • Images were preprocessed via resizing, normalization, and data augmentation to enhance model performance.

2. Model Architecture:

   • A CNN with multiple convolutional, pooling, and fully connected layers was used to effectively classify the breeds.
   • Techniques such as early stopping and learning rate scheduling were applied to improve accuracy.

3. Training & Evaluation:

   • The model was rigorously trained and evaluated to ensure high accuracy, precision, and recall.

4. Django Deployment:

   • The model was deployed into a Django web app where users can upload images and instantly receive breed predictions.

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Outcomes & Future Development

• The application successfully integrates deep learning and web development, providing an accurate and user-friendly breed identification tool.
• Future plans include:
  • Expanding the dataset to cover more dog breeds.
  • Adding detailed breed information, including origin, temperament, and pros/cons of ownership, to assist users in learning more about different breeds.

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How to Run the Project

1. Clone the repository:

   git clone https://github.com/tobibiggest/dog-breed-identification.git

2. Navigate to the project directory and install dependencies:

   cd dog-breed-identification
   pip install -r requirements.txt

3. Run migrations:

   python manage.py migrate

4. Start the Django server:

   python manage.py runserver

5. Open the web application in your browser at http://127.0.0.1:8000 and upload an image to identify the dog's breed.

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Contributing

Contributions are welcome! Please create an issue or submit a pull request for any enhancements or bug fixes.

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License

This project is licensed under the MIT License - see the LICENSE file for details.