Applied Computer Vision: Object Detection and Recognition

Master Computer Vision and Object Recognition with this comprehensive course! Dive into the exciting world of artificial intelligence and learn to build intelligent systems that can "see" and understand images. This course takes you from fundamental concepts to advanced model implementation and evaluation, providing a practical, hands-on experience.

What You'll Learn:

• Image Classification: Build and train a multi-class image classifier using the widely used COCO-2017 dataset. Learn about transfer learning with pre-trained ResNet models and optimize performance through hyperparameter tuning. Identify objects like cats, dogs, and horses with accuracy.
• Object Detection: Explore two powerful object detection models: Faster-RCNN and YOLOv8. Gain practical experience in preparing datasets, training models, and analyzing performance using FiftyOne. Understand both region-based and single-shot detection approaches.
• Semantic Segmentation: Understand semantic segmentation, assigning a class to every pixel in an image. Work with the CamVid dataset and the segmentation_models_pytorch library to train and evaluate a model capable of recognizing objects within complex scenes.
• Instance Segmentation: Go beyond object detection and learn instance segmentation, differentiating between multiple instances of the same object. Build and train a Mask-RCNN model using annotations from the COCO-2017 dataset.
• Essential Tools & Frameworks: Refresh your knowledge of Convolutional Neural Networks (CNNs). Master essential tools like FiftyOne for efficient dataset management and get comfortable with PyTorch through practical tutorials.

Course Highlights:

• Hands-on Exercises: Reinforce your learning through practical, real-world applications.
• Real-World Datasets: Gain experience with industry-standard datasets like COCO-2017 and CamVid.
• Model Evaluation: Learn how to rigorously evaluate the performance of your computer vision models.

This course equips you with the skills to tackle a wide range of computer vision challenges, including image recognition, object identification, and scene understanding. You'll be well-prepared to implement and evaluate various computer vision models for diverse applications.