Data Science Project | Part 3 | Human Segmentation

Welcome to Part 3 of our Data Science Project series!

In this session , we focus on fine-tuning our human segmentation models and evaluating their performance to ensure they are robust and accurate. This segment is crucial for refining your models and preparing them for deployment in real-world applications.

What You'll Learn:

Model Fine-Tuning: Techniques for fine-tuning your segmentation models, including adjusting hyperparameters and transfer learning.

Advanced Training Techniques: Implement strategies like learning rate scheduling, early stopping, and data augmentation to improve model performance.

Model Evaluation: Detailed explanation of evaluation metrics specific to segmentation, such as Intersection over Union (IoU) and Dice Coefficient, and how to interpret these metrics.

Error Analysis: Learn how to perform error analysis to identify common issues and areas for improvement in your segmentation models.

Model Comparison: Compare different models and architectures to select the best-performing one for your application.

Why Watch This session ?

This session is essential for data science practitioners who want to ensure their human segmentation models are both accurate and reliable. By the end of this session , you’ll be equipped with the knowledge to fine-tune, evaluate, and improve your segmentation models for better performance.

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