Day 1: Data Collection and Preprocessing

Session 1: Overview of Computer Vision Project Lifecycles

• Key stages: Data, modeling, and deployment
• Tools and frameworks for end-to-end workflows

Session 2: Data Collection and Annotation

• Techniques for collecting and annotating datasets
• Hands-on lab: Annotating images with CVAT and LabelImg

Session 3: Preprocessing for Computer Vision Models

• Techniques for cleaning, augmenting, and normalizing data
• Hands-on lab: Preprocessing image datasets for training

Day 2: Building Computer Vision Models

Session 1: Training Models for Image Classification

• Exploring architectures like CNNs and transfer learning
• Hands-on lab: Training an image classification model with PyTorch

Session 2: Advanced Techniques: Object Detection and Segmentation

• Implementing YOLO, SSD, and Mask R-CNN
• Hands-on lab: Building an object detection pipeline

Session 3: Evaluating Model Performance

• Metrics for classification, detection, and segmentation
• Practical demonstration: Evaluating models using mAP and IoU

Day 3: Deployment Basics

Session 1: Preparing Models for Deployment

• Exporting and optimizing trained models
• Hands-on lab: Saving and loading models for production

Session 2: Creating APIs for Model Inference

• Using Flask and FastAPI for API development
• Hands-on lab: Serving a computer vision model as an API

Session 3: Containerization with Docker

• Basics of Docker and its role in deployment
• Hands-on lab: Containerizing a computer vision application

Day 4: Advanced Deployment and Monitoring

Session 1: Scaling Deployments with Kubernetes

• Introduction to Kubernetes for container orchestration
• Hands-on lab: Deploying a containerized vision model on Kubernetes

Session 2: Integrating with Cloud Services

• Deploying models on AWS, Azure, and Google Cloud
• Hands-on lab: Deploying a vision model using AWS SageMaker

Session 3: Monitoring and Maintenance

• Tools for tracking performance and detecting drift
• Practical demonstration: Setting up monitoring with Prometheus and Grafana

Day 5: Real-World Applications and Final Project

Session 1: Applications of End-to-End Computer Vision Pipelines

• Case studies in autonomous systems, healthcare, and retail
• Group discussion: Identifying deployment opportunities in your domain

Session 2: Final Project: Developing a Complete Workflow

• Hands-on lab: Building and deploying a computer vision pipeline for a real-world application

Session 3: Review and Future Trends

• Feedback and discussion: Lessons learned and best practices
• Exploring advancements in computer vision and deployment technologies