Deep Learning and Computer Vision: Object Detection with PyTorch

This advanced course on deep learning and computer vision focuses on building, training, and fine-tuning object detection models using PyTorch. Join instructor Prateek Sawhney as he shows you how to work with popular object detection models such as YOLO, SSD, and Faster R-CNN, while gaining hands-on experience with real-world projects. By the end of this course, you'll be prepared to implement and evaluate state-of-the-art object detection systems, fine-tune models for performance, and deploy them in real-time applications.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.

Learning objectives
Build and train advanced object detection models, including YOLO, SSD, and Faster R-CNN, using PyTorch.
Implement object detection systems in real-world scenarios, applying transfer learning techniques to enhance model performance.
Evaluate and fine-tune object detection models using metrics, such as Intersection over Union (IoU) and mean Average Precision (mAP), and optimize them for real-time applications.
Set up and use GitHub Codespaces for collaborative development and cloud-based execution of deep learning projects, streamlining the coding and deployment process.
Create a full object detection system project, from data preprocessing to model training, evaluation, and deployment, ensuring a practical end-to-end understanding of object detection with PyTorch.