Pretrained Models for Image Classification and Object Detection - Part 3 of Computer Vision

Overview

Explore pretrained models for image classification and object detection in this third part of the Computer Vision series.

Syllabus

• Introduction to Pretrained Models
Overview of Pretrained Models in Computer Vision
Advantages of Using Pretrained Models
• Image Classification with Pretrained Models
Commonly Used Pretrained Models for Image Classification
Transfer Learning Techniques for Image Classification
Evaluating Image Classification Models
• Object Detection with Pretrained Models
Overview of Object Detection Tasks
Popular Pretrained Models for Object Detection
Transfer Learning Techniques for Object Detection
Evaluating Object Detection Models
• Implementing Pretrained Models Using Popular Frameworks
Introduction to TensorFlow and PyTorch
Hands-on: Image Classification with TensorFlow/PyTorch
Hands-on: Object Detection with TensorFlow/PyTorch
• Fine-tuning Pretrained Models
Strategies for Fine-tuning Models
Hands-on: Fine-tuning a Model for a Custom Dataset
• Practical Considerations for Using Pretrained Models
Computational Requirements and Optimization
Data Augmentation Techniques
Handling Imbalanced Datasets
• Case Studies and Real-world Applications
Success Stories of Using Pretrained Models
Analyzing Industry-specific Applications
• Final Project and Evaluation
Design and Implement a Project Using Pretrained Models
Presentation and Peer Review of Projects
• Course Summary and Future Directions
Recap of Key Concepts
Emerging Trends in Pretrained Models and Computer Vision
• Additional Resources and References
Suggested Readings and Online Resources
Continuing Learning Paths in Computer Vision

Subjects

Computer Science