The MillionTrees readthedocs should dynamically reflect the size of the datasets. So on the front landing page something like
The MillionTrees Benchmark for Airborne Tree Prediction
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The MillionTrees seeks to collect a million tree locations to create a global benchmark for machine learning models for airborne tree prediction. Machine learning models need large amounts of data to generate realistic predictions. Existing benchmarks often have small amounts of data, often less than 10,000 trees, from single geographic locations and resolutions. The MillionTrees will cover a range of backgrounds, taxa, focal views and resolutions. To make this possible, we need your help!
.. figure:: public/open_drone_example.png
:alt: Image Placeholder
:width: 50%
Current Status
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There are currently 3 datasets available for the MillionTrees benchmark:
* TreeBoxes: A dataset of X tree crowns from y sources
* TreePolygons: A dataset of X tree crowns from y sources
* TreePoints: A dataset of X tree crowns from y sources
Where the docs read the current status of the dataset. We could generate a pre-commit github action, a ipython notebook, or it might be possible for .rst to read a substitution.
The MillionTrees readthedocs should dynamically reflect the size of the datasets. So on the front landing page something like
Where the docs read the current status of the dataset. We could generate a pre-commit github action, a ipython notebook, or it might be possible for .rst to read a substitution.
Current .csv files are
/orange/ewhite/DeepForest/MillionTrees/TreeBoxes_v0.0/official.csv /orange/ewhite/DeepForest/MillionTrees/TreePoints_v0.0/official.csv /orange/ewhite/DeepForest/MillionTrees/TreePolygons_v0.0/official.csv
We could add these to releases to keep track of them, we could read them off of hipergator.