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This comprehensive Udemy course will guide you through the IceVision library, an agnostic computer vision framework for object detection. You will learn how to leverage IceVision's powerful features to build, train, and deploy state-of-the-art object detection models using various deep learning frameworks like fastai and PyTorch Lightning. The course will cover everything from installation and data preparation to advanced topics like custom parsers, data augmentation, model selection, and deployment. Through engaging lectures and hands-on projects, you will gain practical experience in solving real-world computer vision problems.

Dive deep into the world of object detection with IceVision, a versatile and powerful library designed to streamline your computer vision workflows. This course is meticulously crafted to provide you with a solid foundation in object detection, starting from the very basics and progressing to advanced techniques. You will begin by understanding the core concepts of IceVision, including its unique approach to data handling through Parsers and Records, and how to effectively apply various Transforms for robust data augmentation. We will explore how IceVision seamlessly integrates with popular deep learning frameworks such as fastai , enabling you to train cutting-edge models like Faster R-CNN, EfficientDet, and YOLOv5 with ease.

Each section of this course is packed with practical, hands-on projects that reinforce theoretical concepts. You will learn to build custom data pipelines for your unique datasets, implement advanced augmentation strategies, and fine-tune pre-trained models for optimal performance. We will cover essential topics such as model selection criteria, understanding different backbone architectures, and evaluating your models using industry-standard metrics like COCO mAP. Furthermore, the course . By the end of this course, you will possess the skills and confidence to tackle complex object detection challenges, develop robust computer vision solutions, and deploy them in real-world scenarios.