GeoCOCO
tree-labeller
GeoCOCO | tree-labeller | |
---|---|---|
1 | 1 | |
4 | 3 | |
- | - | |
8.8 | 10.0 | |
8 months ago | about 1 year ago | |
Python | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GeoCOCO
tree-labeller
-
Sampling leaves from a tree
I come from a similar application area, where I try to tag (annotation/label) a taxonomy of products iteratively. You are trying something slightly different, AFAIU, labeling a flat set of songs, each song with a set of tags from ontology (directed graph)From an application point of view, this is what taxonomists often do, when migrating products from one catalog to another: mapping one taxonomy to another. There was quite active research on matching ontologies. So, there are tools in both industry and research that help in that process, although I have never researched whether they do it iteratively and using sampling. Another related area is labeling data to train machine learning models (in your case it sounds a bit like multilabel classification, in my case, this is multiclass classification). This is often done iteratively, and tools like Explosion Prodigy samples for manual annotation only those items that the ML model is still not confident enough. This might be offtopic, but I looked at your library and your notation for defining relations between tags, reminded me of RDF and OWL languages for defining ontologies. They are quite well-defined and have tools for making inferences (reasoners).
What are some alternatives?
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
qgis-earthengine-examples - A collection of 300+ Python examples for using Google Earth Engine in QGIS
WhiteboxTools-ArcGIS - ArcGIS Python Toolbox for WhiteboxTools
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/opencv/cvat]
tpkutils - ArcGIS Tile Package Utilities