U-Net++ResNet18--Images From Scratch for Image Segmentation - PyTorch (Part 03)

Welcome to our 4-part series on U-Net++—Images From Scratch for Image Segmentation in PyTorch! In this video, we'll focus on understanding the architecture and implementation details of U-Net++.
If you missed the other parts , you can find the links below.
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Overview
This Technical Presentation on August 2, 2025, features presenter Samuel Oke Oladipupo, from NeuralRow, discussing unit architecture for image segmentation.
Samuel gives an in-depth presentation on U-Net and its advanced version, U-Net++, using a baby-cat analogy to explain #ImageSegmentation and the architecture’s initial application in #MedicalImaging. He covers the model training process, sharing challenges encountered with skip connections and dimensions while using #GitHub Codespaces, and highlights pitfalls like the unreliability of #ChatGPT for generating U-Net code. Samuel demonstrates model execution. He simulates the use of #CPU instead of #GPU led to a sluggish training process and a validation error, prompting collaborative troubleshooting discussions.
Notes:
🎓 Introduction and Technical Issues
Topic: Unit architecture for image segmentation
🧠 U-Net Architecture Explanation
U-Net and U-Net++ architectures explained
U-Net++ is an extension of U-Net with dense skip pathways
Image segmentation explained using a baby-cat analogy
U-Net++ initially introduced for medical imaging
Data set structure: #images and #masks explained

Presenter: Oke Eniifeoluwa Samuel

U-Net++ResNet18--Images From Scratch for Image Segmentation - PyTorch (Part 01)

U-Net++ResNet18--Images From Scratch for Image Segmentation - PyTorch (Part 02)

#NeuralRow #UNet #OpenAI #DataScience #MachineLearning #UNetplusplus #AI