🐶 Dog Breed Classifier

Deep Learning Image Classification Project (Google Colab)

📌 Overview

The Dog Breed Classifier is a deep learning project that predicts the breed of a dog from an input image. The model is trained to classify 120 different dog breeds using a dataset of 10,000+ labeled images sourced from Kaggle.

The project leverages transfer learning with a pre-trained MobileNetV2 architecture to achieve efficient and accurate multi-class image classification.

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🚀 Features

• 🧠 Predicts 120 dog breeds from images
• ⚡ Uses transfer learning for faster training and better generalization
• 📊 Evaluates performance using precision, recall, and confusion matrix
• ☁️ Trained and tested on Google Colab (T4 GPU)
• 🔍 Supports real-world testing on unseen images

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🛠️ Tech Stack

• Language: Python
• Deep Learning: TensorFlow, Keras
• Model Architecture: MobileNetV2 (Transfer Learning)
• Data Processing: NumPy, Pandas
• Visualization: Matplotlib, Seaborn
• Platform: Google Colab (GPU-enabled)

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📂 Dataset

• Source: Kaggle
• Size: 10,000+ images
• Classes: 120 dog breeds
• Images are organized by breed folders and split into training and validation sets.

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🧪 Methodology

1️⃣ Data Preprocessing

• Resized images to match MobileNetV2 input dimensions
• Normalized pixel values
• Applied data augmentation (rotation, flipping, zoom) to reduce overfitting

2️⃣ Model Architecture

• Used MobileNetV2 as the base model with pre-trained ImageNet weights
• Added custom fully connected layers for multi-class classification
• Froze base layers initially and fine-tuned for better performance

3️⃣ Training

• Trained using categorical cross-entropy loss
• Optimized with Adam optimizer
• Used Google Colab T4 GPU to reduce training time

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📊 Evaluation

The model performance was evaluated using:

• Confusion Matrix
• Precision & Recall
• Overall Classification Accuracy

Hyperparameters were fine-tuned to improve accuracy and generalization on unseen data.

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🧪 Testing & Deployment

• Tested the trained model on sample dog images
• Verified predictions for real-world usability
• Model can be extended into a web or mobile application for deployment

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📌 How to Run (Google Colab)

1. Open the notebook in Google Colab
2. Enable GPU: Runtime → Change runtime type → GPU
3. Upload the dataset or connect Kaggle API
4. Run all cells sequentially
5. Test the model with sample images

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🎯 Future Improvements

• Improve accuracy with larger datasets
• Experiment with advanced architectures (EfficientNet, ResNet)
• Deploy as a web application using Flask or FastAPI

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👨‍💻 Author

Kaif Ali Khan Computer Science Undergraduate | ML & Full-Stack Developer

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