Computer Vision, Deep Learning and CRF tutorials for the course Foundation of Digital Humanities (Fall 2019)
To access previous years tutorials you can switch to branch v.2018
- Clone :
git clone https://github.com/dhlab-epfl/fdh-tutorials.git - or download the zip directly
- Install Anaconda with Python 3.* : https://www.anaconda.com/download/
- Install from yml file:
conda env create -f environment.yml - Activate the environment:
conda activate fdh_tutorialorsource activate fdh_tutorial - Download spacy italian model (other models can be found on this page)
python -m spacy download it_core_news_sm - (Optional) if you want to run the 3-application/cv-faces notebook, install dlib:
pip install dlib - Start a Jupyter Notebook Server:
jupyter notebook - Once you're done with the exercice:
source deactivate
Known issues
- On MacOS the
sklearn-crfsuitemight not get correctly installed with conda. If you're having an error try installing it (in thefdh_tutorialenvironment) with:
conda install -c conda-forge python-crfsuite
conda env update -f environment.yml
conda env update -f environment.yml
Through Jupyter, open the notebook files (.ipynb) in the different directories of this very repository.
Inside the computer-vision-deep-learning folder you will find:
- Image processing basics: get familiar with the principal concepts in image processing and computer vision (image handling, filters, denoising)
- Deep learning: Tensorflow basics with MNIST example (single layer network and multilayer network)
- Applications:
- CV: Face detection using Convolutional Neural Networks (CNN)
- DL: Historical document processing - page and textline detection
- DL: Historical document classification
Inside the crffolder you will find a CRF tutorial notebook treating how to use a CRF from annotating to predicting new text.
If you're already familiar with some concepts, feel free to go to the next notebook.