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Dog Breed Classification using Deep Learning

Overview

This project focuses on classifying dog breeds from images using deep learning. The model is fine-tuned on the MobileNetV2 architecture and trained on the Kaggle Dog Breed Identification dataset. The implementation was done in Google Colab using TensorFlow and Keras.

Dataset

Model

  • Pretrained Model: MobileNetV2 (TensorFlow 2)
  • Fine-Tuning: Trained on the Kaggle dataset with additional layers added for classification
  • Input Size: 224x224 pixels
  • Optimization: Adam optimizer with categorical cross-entropy loss
  • Evaluation: Accuracy and loss metrics

Implementation

Technologies Used

  • Python
  • TensorFlow & Keras
  • Google Colab
  • NumPy, Pandas, Matplotlib,Tensor Flow

Steps

  1. Data Preprocessing

    • Loaded images from the Kaggle dataset
    • Resized images to 224x224 pixels
    • Normalized pixel values
    • One-hot encoded the labels

  2. Model Training

    • Used MobileNetV2 as the base model
    • Added fully connected layers for classification
    • Fine-tuned the model on the dataset
    • Used data augmentation to improve generalization

  3. Evaluation

    • Split dataset into training and validation sets
    • Measured accuracy and loss
    • Tested on unseen images

  4. Deployment (Future Work)

    • Integrating the model into a Kotlin Jetpack Compose App
    • Using CameraX for real-time image capture

How to Use

  1. Clone the repository: 
    git clone https://github.com/yourusername/dog-breed-classification.git
cd dog-breed-classification
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run the training script: 
    python train.py
  4. Test the model: 
    python predict.py --image path/to/image.jpg

Results

  • Achieved high accuracy in breed classification
  • Improved performance using fine-tuning and data augmentation
  • Future goal: Deploy in a mobile app for real-time prediction

Future Improvements

  • Optimize model inference speed for mobile deployment
  • Improve accuracy with additional data augmentation techniques
  • Implement a lightweight version for edge devices

Acknowledgments

  • Kaggle for providing the dataset
  • Google for the MobileNetV2 model
  • TensorFlow & Keras for deep learning support
  • Google colab for programming