Data Science Project | Part 2 | Human Segmentation

Welcome to Part 2 of our Data Science Project series!
In this session, we continue our deep dive into the world of human segmentation using deep learning. This segment focuses on advanced model building and training techniques to enhance the accuracy and performance of our segmentation models.
What You'll Learn:
- Model Architecture: Explore advanced architectures like U-Net, Mask R-CNN, and DeepLab, and understand how they improve segmentation tasks.
- Data Augmentation: Learn advanced data augmentation techniques to increase the diversity of your training data, leading to better model generalisation.
- Training the Model: Step-by-step guide on setting up the training process, including loss functions, optimisers, and training loops.
- Handling Class Imbalance: Techniques to manage imbalanced datasets commonly encountered in segmentation tasks.
-Performance Metrics: Introduction to key performance metrics specific to segmentation, such as Intersection over Union (IoU) and Dice Coefficient.
Why Watch This session?
This session is essential for data science practitioners looking to deepen their understanding of human segmentation. By the end of this session, you’ll have the skills to build and train more sophisticated segmentation models, preparing you for real-world applications.
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