biigle / maia
BIIGLE module module for the Machine Learning Assisted Image Annotation method.
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Requires
- biigle/largo: ^2.25 || dev-master
- dev-master
- v2.6.3
- v2.6.2
- v2.6.1
- v2.6.0
- v2.5.7
- v2.5.6
- v2.5.5
- v2.5.4
- v2.5.3
- v2.5.2
- v2.5.1
- v2.5.0
- v2.4.0
- v2.3.5
- v2.3.4
- v2.3.3
- v2.3.2
- v2.3.1
- v2.3.0
- v2.2.1
- v2.2.0
- v2.1.1
- v2.1.0
- v2.1.0-beta3
- v2.1.0-beta2
- v2.1.0-beta
- v2.0.2
- v2.0.1
- v2.0.0
- v2.0.0-beta4
- v2.0.0-beta3
- v2.0.0-beta2
- v2.0.0-beta
- v1.24.0
- v1.23.0
- v1.22.1
- v1.22.0
- v1.21.2
- v1.21.1
- v1.21.0
- v1.20.0
- v1.19.0
- v1.18.2
- v1.18.1
- v1.18.0
- v1.17.13
- v1.17.12
- v1.17.11
- v1.17.10
- v1.17.9
- v1.17.8
- v1.17.7
- v1.17.6
- v1.17.5
- v1.17.4
- v1.17.3
- v1.17.2
- v1.17.1
- v1.17.0
- v1.16.0
- v1.15.1
- v1.15.0
- v1.14.1
- v1.14.0
- v1.13.0
- v1.12.0
- v1.11.2
- v1.11.1
- v1.11.0
- v1.10.1
- v1.10.0
- v1.9.0
- v1.8.0
- v1.7.1
- v1.7.0
- v1.6.2
- v1.6.1
- v1.6.0
- v1.5.1
- v1.5.0
- v1.4.3
- v1.4.2
- v1.4.1
- v1.4.0
- v1.3.0
- v1.2.5
- v1.2.4
- v1.2.3
- v1.2.2
- v1.2.1
- v1.2.0
- v1.1.0
- v1.0.3
- v1.0.2
- v1.0.1
- v1.0.0
- v0.1.0
- dev-dependabot/npm_and_yarn/npm_and_yarn-a9d12a0507
- dev-torch2
- dev-sagun-classification
- dev-model-storage
This package is auto-updated.
Last update: 2024-11-06 10:51:18 UTC
README
This is the BIIGLE module module for the Machine Learning Assisted Image Annotation method.
Installation
- Run
composer require biigle/maia
. - Install the Python dependencies with
pip install -r requirements.txt
. - Add
Biigle\Modules\Maia\MaiaServiceProvider::class
to theproviders
array inconfig/app.php
. - Run
php artisan vendor:publish --tag=public
to publish the public assets of this module. - Run
docker-compose exec app php artisan migrate
to create the new database tables. - Configure a storage disk each for the training proposal and annotation candidate patches. Set the
MAIA_TRAINING_PROPOSAL_STORAGE_DISK
andMAIA_ANNOTATION_CANDIDATE_STORAGE_DISK
variables in the.env
file to the name of the respective storage disk. Do not use the same disk for both! The content of the storage disks should be publicly accessible. Example for local disks:// MAIA_TRAINING_PROPOSAL_STORAGE_DISK 'maia-tp' => [ 'driver' => 'local', 'root' => storage_path('app/public/maia-tp-patches'), 'url' => env('APP_URL').'/storage/maia-tp-patches', 'visibility' => 'public', ], // MAIA_ANNOTATION_CANDIDATE_STORAGE_DISK 'maia-ac' => [ 'driver' => 'local', 'root' => storage_path('app/public/maia-ac-patches'), 'url' => env('APP_URL').'/storage/maia-ac-patches', 'visibility' => 'public', ],
This requires the linkstorage -> ../storage/app/public
in thepublic
directory.
Configuration
New processing jobs are submitted to the default
queue of the gpu
connection. You can configure these with the MAIA_JOB_QUEUE
and MAIA_JOB_CONNECTION
environment variables. Optionally, you can configure a different queue (but not connection) fot the jobs to generate feature vectors with MAIA_FEATURE_VECTOR_QUEUE
. This can be used to give jobs to generate feature vectors a higher priority.
References
Reference publications that you should cite if you use MAIA for one of your studies.
-
BIIGLE 2.0 Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections. Frontiers in Marine Science, 4, 83. doi:
10.3389/fmars.2017.00083
-
MAIA Zurowietz, M., Langenkämper, D., Hosking, B., Ruhl, H. A., & Nattkemper, T. W. (2018). MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration. PloS one, 13(11), e0207498. doi:
10.1371/journal.pone.0207498
Developing
Take a look at the development guide of the core repository to get started with the development setup.
Want to develop a new module? Head over to the biigle/module template repository.
Contributions and bug reports
Contributions to BIIGLE are always welcome. Check out the contribution guide to get started.