Computer Vision Portfolio

Advanced computer vision portfolio featuring neural network architectures and deep learning implementations for image classification and landmark recognition.

Projects

1. Landmark Classification

Location: projects/landmark-classification/

A comprehensive deep learning project for landmark recognition using convolutional neural networks (CNNs).

Features:

• Custom CNN architecture with 4 convolutional blocks
• Batch normalization and dropout for regularization
• Transfer learning implementation
• Data preprocessing and augmentation
• Model training and evaluation pipeline

Technology Stack:

• PyTorch for deep learning framework
• Custom CNN architecture design
• Data loaders and transformations
• Model checkpointing and evaluation

Key Components:

• src/model.py - CNN architecture definition
• src/transfer.py - Transfer learning implementation
• src/data.py - Data loading and preprocessing
• src/helpers.py - Utility functions
• src/create_submit_pkg.py - Model packaging

Technical Skills Demonstrated

• Deep Learning Frameworks: PyTorch
• Neural Network Architectures: CNNs, Transfer Learning
• Computer Vision: Image Classification, Feature Extraction
• Model Optimization: Batch Normalization, Dropout, Adaptive Pooling
• Data Processing: Image Preprocessing, Data Augmentation
• Model Deployment: Model Packaging and Submission

Architecture Highlights

Custom CNN Model

• Input: 3-channel RGB images
• Architecture: 4 convolutional blocks with increasing depth (64→128→256→512)
• Regularization: Batch normalization and dropout
• Pooling: MaxPooling and Adaptive Average Pooling
• Output: Configurable number of classes (default 1000)

Key Features

• Progressive feature extraction with deeper layers
• Batch normalization for training stability
• Configurable dropout for overfitting prevention
• Adaptive pooling for flexible input sizes

Project Structure

projects/
└── landmark-classification/
    └── src/
        ├── model.py          # CNN architecture
        ├── transfer.py       # Transfer learning
        ├── data.py           # Data processing
        ├── helpers.py        # Utilities
        └── create_submit_pkg.py # Model packaging

Future Enhancements

• GAN implementations for image generation
• Object detection with YOLO/R-CNN
• Semantic segmentation projects
• Real-time computer vision applications
• Model optimization and quantization

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This portfolio demonstrates production-ready computer vision implementations with modern deep learning techniques.