Project Overview
The Dog Breed Classifier is an image classification project that leverages deep learning to identify different breeds of dogs from images. Using convolutional neural networks (CNNs), this classifier has been trained to recognize 133 different dog breeds with high accuracy.
The project demonstrates the power of transfer learning by utilizing pre-trained models that have been fine-tuned on a large dataset of dog images. This approach allows the classifier to achieve impressive results even with a relatively small amount of training data for each breed.
Technologies Used
The project primarily uses PyTorch as the deep learning framework, leveraging its powerful capabilities for building and training neural networks. The model architecture includes pre-trained networks like VGG, Alexnet, and DenseNet for transfer learning.
Key Features
- Recognition of 133 different dog breeds with high accuracy
- Human face detection - can identify if a human is in the image
- Breed resemblance detection - tells humans which dog breed they most resemble
- Multiple pre-trained model options (VGG, Alexnet, DenseNet)
- Robust image preprocessing pipeline for consistent results
- Custom CNN architecture with transfer learning for optimal performance
Implementation Details
Data Preprocessing
Images are resized, normalized, and augmented to ensure consistent input to the neural network. Data augmentation techniques like random rotations, flips, and color jittering were applied to increase the diversity of the training set.
Model Architecture
The classifier uses transfer learning with pre-trained CNN models (VGG, Alexnet, DenseNet) as a base, with custom classification layers added on top. These pre-trained networks have already learned to extract meaningful features from images, which are then fine-tuned for the specific task of dog breed classification.
Training Process
The model was trained using stochastic gradient descent with a learning rate scheduler to ensure optimal convergence. Early stopping was implemented to prevent overfitting, and the best-performing model was saved for inference.
Results & Impact
The final model achieves over 85% accuracy on the test dataset, demonstrating its effectiveness in distinguishing between various dog breeds. The classifier works well even with challenging images where the dog may be partially obscured or in unusual poses.
The project showcases practical applications of deep learning in image classification tasks and provides a foundation for more advanced computer vision projects.
Challenges & Learning
Some of the main challenges encountered during this project included:
- Handling class imbalance in the training dataset
- Fine-tuning hyperparameters for optimal performance
- Optimizing the model for both accuracy and inference speed
- Integrating the human face detection component seamlessly
These challenges provided valuable learning opportunities in deep learning model optimization, transfer learning techniques, and computer vision best practices.