Скачать с ютуб Applying Permutation Channel Importance (PCI) to a Remote Sensing Model | Python Tutorial в хорошем качестве

Applying Permutation Channel Importance (PCI) to a Remote Sensing Model | Python Tutorial

🚀 Course 🚀 Free: https://adataodyssey.com/permutation-... Paid: https://adataodyssey.com/courses/xai-... We dive into Permutation Channel Importance (PCI) and show you how to apply it using Python. We'll work with the Landsat Irish Coastal Segmentation (LICS) Dataset, a resource for advancing deep learning methods in coastal water body segmentation. This dataset includes 100 multispectral test images, each with a binary segmentation mask that classifies pixels as either land or ocean. This is a great start if you are interested in applying Explainable AI methods to remote sensing machine learning models. 🚀 Useful playlists 🚀 XAI for CV:    • XAI for CV   XAI:    • Explainable AI (XAI)   SHAP:    • SHAP   Algorithm fairness:    • Algorithm Fairness   🚀 Get in touch 🚀 Medium:   / conorosullyds   Threads: https://www.threads.net/@conorosullyds Twitter:   / conorosullyds   Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Introduction 01:43 Landsat Irish Coastal Segmentation (LICS) dataset 02:54 Exploring LICS 09:58 Shuffling a channel 12:53 Applying PCI