constantinpape/03_learned_representations

An execise for using features extracted from a vision transformer for downstream tasks

Exercise: Transformer Representation

An exercise for utilizing features extracted from a vision transformer for downstream tasks.

This exercise has two parts. In the first part, we'll learn how to extract features for a batch of images from the DINOv2 vision transformer model, and apply dimentionality reduction and clustering on those features.
In the second part, we will train a model on top of those extracted features for the segmentation task.

Setup

All the neccessary files are included in this repo. You just need to setup the python environment by running this script:

source setup.sh

After this, make sure you are in the base environment and then run jupyter lab:

mamba activate base
jupyter lab

TA Info

To convert solutions python files into notebooks and generate the exercises, first, please install jupytext and nbconvert. Afterward, run python ./generate_exercise <input_file.py> .