├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md ├── data.md └── lessons ├── 01_lesson ├── 01_classification.ipynb ├── 01_notes.md ├── 01_segment.ipynb └── 01_segmentation_herbarium.ipynb ├── 02_lesson └── TribunalSorter.ipynb └── 03_lesson └── iiif_resize_experiment.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. 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Affirmer understands and acknowledges that Creative Commons is not a 120 | party to this document and has no duty or obligation with respect to 121 | this CC0 or use of the Work. 122 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # fastai4GLAMS study group 3 | A study group for v4 of the fastai [Practical Deep Learning for Coders course](https://course.fast.ai/) with a focus on applications in [GLAM (galleries, libraries, archives, and museums)](https://en.wikipedia.org/wiki/GLAM_%28industry_sector%29) 4 | 5 | Nicole Coleman is running a [Elements of AI 4 GLAM study group](https://github.com/cncoleman/elementsofai4glam) which you might also want to check out as an alternative. 6 | > "Elements of AI is a free online course developed at the University of Helsinki that addresses the theory behind AI. The course is intended for people who want to learn what AI is, what can and cannot be done with AI. The study group provides a context for the material." 7 | 8 | ## Updates: 9 | - 2020-09-01: Notebooks based on the course lesson notebooks can be found in [lessons](lessons/) 10 | - 2020-08-21: the course has now launched. See [thread](https://forums.fast.ai/t/glams-galleries-libraries-archives-and-museums-fastai-study-group/76064/19) for discussion 11 | - 2020-08-06: discussion thread for the study group now live on the forum: https://forums.fast.ai/t/glams-galleries-libraries-archives-and-museums-fastai-study-group/76064?u=danielvs 12 | 13 | ## tl;dr 14 | a study group: 15 | - following v4 of the [fastai](https://www.fast.ai/) [Practical Deep Learning for Coders](https://course.fast.ai/) course with a focus on applications in GLAM settings 16 | - focused on helping each other out 17 | - tackling domain specific issues 18 | - has a practical focus 19 | 20 | 21 | ## Practical Deep Learning for Coders, v4 22 | This course will follow v4 of the [fastai](https://www.fast.ai/) [Practical Deep Learning for Coders](https://course.fast.ai/) which ~should be released sometine in July 🤞~ is available at https://course.fast.ai/ 23 | 24 | ### What the course covers: 25 | - 🤔 Ethics 26 | - 👀 CV (Computer vision) 27 | - 📖 NLP (Natural Language Processing) 28 | - 🗂 Tabular Data 29 | - 🤖 Deployment of models 30 | - 🐍 I have picked up ++ Python from doing previous versions of this course 31 | 32 | ### Why you might want to do the course? 33 | 34 | There is a lot of interest in applying AI/ machine learning in GLAM settings with a range of potential applications being explored. There is also sometimes the perception that machine learning is very hard or can only be done by large tech companies. The fastai course aims to make deep learning (a branch of machine learning) acccesible while not hiding the important underlying theory. As a result I think this course is could be very useful for people working in/with GLAM instutions because: 35 | 36 | - It will allow you to start using deep learning in a hands-on way, and you may be able to solve problems at work using machine learning 37 | - Doing things yourself will give you a much better sense of what is possible and how things work 38 | - If you collaborate with computer scientists, commercial companies etc. on ai projects, the course should help you to: 39 | - a) be better able to communicate with them 40 | - b) have a better sense of whether a vendor or commerical partner is overpromising 41 | - c) be more aware of things about the GLAM setting that can pose challenges for machine-learning approaches 42 | - 😃 It's fun! 43 | 44 | ### Prerequisites 45 | There is no particular prerequisite for joining the study group. The fastai course assumes you have been coding for at least a year with the course using Python. There is *some* maths in the course but it is explained very clearly and I really wouldn't worry let math worries stop you from doing the course. If there are things you don't understand you can fill in the missing pieces as you go rather than wasting time learning things you might not need before getting started. 46 | 47 | ## How the study group will work 48 | This study group will be primarily asynchronous with discussions taking place on the fastai [forums](https://forums.fast.ai/). The reason this has been chosen over a slack group is that: 49 | - there is already a ton of expertise on the fastai forums 50 | - a slack channel is semi-private which means it's a bigger barrier to join and discussions get lost 51 | - the forum discussions can be a resource that people might stumble upon through a google search in the future 52 | 53 | The suggested approach will be: 54 | - 🍿 watch the fastai course videos and work through the notebooks in your own time 55 | - 🕸 use the study group forum thread to discuss the lessons, ask questions and share resources 56 | - ✨ since data is so fundamental for deep-learning we may want to curate some labelled datasets which can be used to apply what is learned in the lessons. 57 | - 📞 if there is interest we can set up a video call(s) at some point during the course 58 | 59 | ### This repository 60 | I will add notebooks to this repository based on materials from the fastai course to using GLAM data and will happily accept pull-requests for other notebooks. 61 | 62 | ### Schedule 63 | When I did the course in 2019 I usually did one lesson per week. This comprises around ~2 hours to watch the video and then variable amount of time adapting the notebooks and trying to apply what is covered in the lessons doing follow up reading etc. The amount of time spent can be adjusted depending on your interest in each application and your own schedule. 64 | 65 | ## How to join 66 | 67 | - 🔔 The new version of the fast.ai course will [launch on 21 August](https://forums.fast.ai/t/fastai2-and-new-course-release-date-aug-21st/75684). ~If you want to get an email reminder when the course starts add your email to the [sign-up](https://forms.gle/DTbdnhD571fdrdxz7).~ 68 | - Join the [thread](https://forums.fast.ai/t/glams-galleries-libraries-archives-and-museums-fastai-study-group/76064?u=danielvs) on the fast.ai forums! 69 | 70 | ## Yummy GLAM machine learning data 🖼 71 | [Suggested datasets for working with the course material](/data.md) 72 | 73 | -------------------------------------------------------------------------------- /data.md: -------------------------------------------------------------------------------- 1 | # GLAM data for using with fastai course materials 2 | 3 | Below are some potential datasets that could be used with course materials. They have been suggested because they meet the following criteria: 4 | 5 | 1. they are openly available 6 | 2. most are already have labels 7 | 3. most are small enough to work with interactively 8 | 9 | ## Contributing 10 | 11 | If you know of other GLAM related datasets that might work well then please feel free to make a pull request or open an issue. I would suggest restricting this list to things which meet the first criteria above i.e the dataset isn't behind a paywall/subscription. 12 | 13 | # Images 🖼 14 | 15 | ## Classification 16 | ### Internet Archive 'judge a book by its cover' 17 | > Classifcation of book covers into 'useful' or 'not useful' 18 | - Type: Image classification (two classes) 19 | - Data size: 1.4GB 20 | - Link: https://archive.org/details/year-1923-not-very-useful-covers 21 | 22 | ### iMet Collection 2020 - FGVC7 23 | > "Recognize artwork attributes from The Metropolitan Museum of Art" 24 | - Type: Multi-label image classification 25 | - Data size: 27.44 GB 26 | - Link: [https://www.kaggle.com/c/imet-2020-fgvc7]() 27 | 28 | ### iMet Collection 2019 - FGVC6 29 | >"Recognize artwork attributes from The Metropolitan Museum of Art" 30 | - Type: Multi-label image classification 31 | - Data size: 22.65 GB 32 | - Link: [https://www.kaggle.com/c/imet-2019-fgvc6/overview]() 33 | 34 | ### Iconclass AI Test Set 35 | >"A test dataset and challenge to apply machine learning to collections described with the Iconclass classification system." 36 | - Type: Multi-label 37 | - Data size: 3.1 GB 38 | - Link: [https://labs.brill.com/ictestset/]() 39 | 40 | ## Object detection 41 | 42 | ### Newspaper Navigator 43 | >"This dataset consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. " 44 | - Type: object detection, classification 45 | - Data sizes: varies 46 | - Link: [https://github.com/LibraryOfCongress/newspaper-navigator]() 47 | 48 | 49 | --- 50 | # Text 📖 51 | 52 | ## Classification 53 | 54 | ### UK Selective Web Archive Website Classification Dataset 55 | > "We are particularly interested in understanding whether high-level metadata like this can be used to train an appropriate automatic classification system so that we might use this manually generated dataset to partially automate the categorisation of our larger archives. We expect that a appropriate classifier might require more information about each site in order to produce reliable results, and are looking at augmenting this dataset with further information in the future." 56 | - Type: Classification 57 | - Data size: 3.01 MB 58 | - Link: [https://doi.org/10.5259/ukwa.ds.1/classification/1]() 59 | 60 | ### Books divided by Genre from the Digitised 19th century books dataset 61 | >"A dataset derived from the Digitised 19th Century Books dataset which classifies the books by genre (Drama, Poetry, Prose, Music and unidentified)." 62 | - Type: Classification 63 | - Data size: < 1GB 64 | - link: https://bl.iro.bl.uk/work/ff82a4ff-12a3-4abe-8108-2c9b1172ccc4 65 | - notes: There are likely to be some errors in the labels. 66 | 67 | --- 68 | # Tabular 🗂 69 | 70 | ### Books divided by Genre from the Digitised 19th century books dataset 71 | >"A dataset derived from the Digitised 19th Century Books dataset which classifies the books by genre (Drama, Poetry, Prose, Music and unidentified)." 72 | - Type: Classification 73 | - Data size: < 1GB 74 | - link: https://bl.iro.bl.uk/work/ff82a4ff-12a3-4abe-8108-2c9b1172ccc4 75 | - notes: There are likely to be some errors in the labels. 76 | 77 | 78 | ### UK Selective Web Archive Website Classification Dataset 79 | > "We are particularly interested in understanding whether high-level metadata like this can be used to train an appropriate automatic classification system so that we might use this manually generated dataset to partially automate the categorisation of our larger archives. We expect that a appropriate classifier might require more information about each site in order to produce reliable results, and are looking at augmenting this dataset with further information in the future." 80 | - Type: Classification 81 | - Data size: 3.01 MB 82 | - Link: [https://doi.org/10.5259/ukwa.ds.1/classification/1]() 83 | -------------------------------------------------------------------------------- /lessons/01_lesson/01_notes.md: -------------------------------------------------------------------------------- 1 | ## Lesson 01 notes 2 | Daniel van Strien's notes 3 | These notes are very rough, I will try and come back and tidy them one day... 4 | 5 | ### what's covered in the lesson? 6 | 7 | - an intro to the course, library and fastai organization 8 | 9 | ### tl;dr (takeaways) 10 | These were some key takeaways from lesson 1 for me 11 | 12 | #### Models give you predictions - don't tell you what to do. NOT creating actions, creating predictions 13 | It is really useful to be reminded of this from time to time. 14 | 15 | #### Models operate in an environment 16 | Again I think this is something that should be considered all the time. There seem to often be two responses types of responses to the outputs of ml models from people, either the model is complete shit because it doesn't get a few predictions right, or it's amazing because of it only misses a few predictions. I think it's really important to consider what the 'environment' these predictions are operating in (i.e. some experimental project with a GLAM collection vs a live catalogue system). 17 | 18 | 19 | ### How much data/how many labels? 20 | 21 | Not enough labels? 22 | need for deep learning will almost always come from a lot of data 23 | but not enough labelled data 24 | 25 | 26 | A model operates in an environment - how does a model interact with the environment 27 | 28 | - where do we use ai in GLAM settings. which environments does deep-learning get deployed in? 29 | 30 | Biased data? 31 | 32 | - example from policing and arrests, feedback loops ? 33 | 34 | This is obviously an issue with GLAMs too, for example how extensive our certain things catalogued. this could be deeply harmful or relatively benign 35 | 36 | - Proxies - data is a proxy for something else you actually care about! 37 | - metadata can also be used as a proxy for something else 38 | 39 | 40 | ### Python code 41 | 42 | - import * makes some people sad but in fastai is done by defining `__all__` http://xion.io/post/code/python-all-wild-imports.html 43 | 44 | 45 | ### fastai aplications 46 | - vision 47 | - text 48 | - tabular 49 | - colab filter 50 | 51 | Doc function can be used to find out what a function does. doc() 52 | 53 | Documentation is written in Jupyter notebooks :) so you can run the documentation examples? 54 | 55 | Library written in nbdev which is *chef kiss* 56 | 57 | 58 | 59 | 60 | 61 | --------------------------------------------------------------------------------