Segmentation

Topics covered

The course deals with Semantic Image Segmentation using CNNs. Students will learn how to solve simple segmentation problems with PyTorch.

• Image Segmentation Basics
• Basic Building blocks of Segmentation CNNs
• Commonly used architectures
• How to implement/train our own CNN
• Hands On: Students will implement their own Segmentation CNN with PyTorch

Course Materials

Please take note of the Prerequisites

Lecture Notes

• slides

Exercise Material

• Exercises
• Solutions

Prerequisites

Note that there is a certain amount previous knowledge required to fully take advantage of the course. In addition some software has to be installed prior to the course so time is not wasted during the actual exercises.

Required Previous Knowledge

• Python: This course uses Python during the exercises.
• Basics: The basics of deep learning should be known.

Required Software

The same software as for the deep learning basics workshop is required.

In addition to that please make sure that you have installed the following Python packages:

• PyTorch
• Numpy
• Pillow
• Matplotlib
• Tqdm
• Tensorboard
• Albumentations
• Scikit-learn
• LabelMe

Required Datasets

Please download the PASCAL VOC training / val data.