Deep Learning Image Classification System
A production-ready computer vision system built with TensorFlow and PyTorch for real-time image classification. Features custom CNN architectures, transfer learning, data augmentation, and deployed as a scalable REST API with 95%+ accuracy on multiple image categories.
Deep Learning
Computer Vision
TensorFlow
PyTorch
CNN
REST API
Docker
AWS
#Deep Learning Image Classification System
This project demonstrates advanced computer vision capabilities through a production-ready image classification system. Built using state-of-the-art deep learning frameworks, the system can accurately classify images across multiple categories with real-time inference capabilities.
#Key Features
- Custom CNN Architecture: Designed and implemented custom convolutional neural networks optimized for specific image classification tasks
- Transfer Learning: Leveraged pre-trained models (ResNet, VGG, EfficientNet) for improved performance and faster training
- Data Augmentation: Implemented sophisticated data augmentation techniques to improve model generalization
- Real-time Inference: Optimized model for real-time predictions with sub-second response times
- Scalable Deployment: Containerized application deployed on AWS with auto-scaling capabilities
- Model Monitoring: Integrated MLOps practices for continuous model monitoring and performance tracking
#Technical Implementation
#Model Architecture
- Implemented custom CNN with attention mechanisms
- Used progressive resizing and mixed precision training
- Applied dropout and batch normalization for regularization
- Achieved 95%+ accuracy on validation dataset
#Deployment Pipeline
- Dockerized application with REST API endpoints
- Implemented CI/CD pipeline with GitHub Actions
- Deployed on AWS ECS with load balancing
- Set up monitoring and logging with CloudWatch
#Performance Optimization
- Model quantization for edge deployment
- TensorRT optimization for GPU inference
- Batch processing for high-throughput scenarios
- Memory optimization for mobile deployment
#Technologies Used
- Deep Learning: TensorFlow, PyTorch, Keras
- Computer Vision: OpenCV, PIL, Albumentations
- API Development: FastAPI, Flask
- Deployment: Docker, AWS ECS, AWS Lambda
- Monitoring: MLflow, Weights & Biases
- Data Processing: NumPy, Pandas, OpenCV
#Results and Impact
- Achieved 95.3% accuracy on test dataset
- Reduced inference time by 60% through optimization
- Successfully deployed to production serving 10k+ predictions daily
- Implemented automated retraining pipeline with 99.9% uptime
This project showcases expertise in end-to-end AI system development, from research and experimentation to production deployment and monitoring.