AgML v0.6.2
This release introduces a new export tool for AgML datasets, various expansions to methods, and some bugfixes.
Major Changes
- A new method
agml.data.exporters.export_yolo()has been added (alongside a companion wrapper to a dataset,loader.export_yolo()), that enables exporting datasets to the Ultralytics YOLO format. The resulting datasets can be directly integrated in one line of code into Ultralytics/Darknet YOLO training pipelines, which augments AgML's object detection resources.
Improvements
- Splitting datasets, specifically with multi-dataset loaders, now works properly.
Bugfixes
- Added an
export_tensorflow()method for multi-loaders, which was previously missing. - Updated Helios compilation instructions to be compatible with the C++17 standard needed for updated Helios versions.
- All example notebooks have been updated to be consistent with new AgML methods.
- AgML visualization methods now no longer double-display in Jupyter notebooks.