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AgML 0.7.0

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@amogh7joshi amogh7joshi released this 26 Oct 15:38
· 10 commits to main since this release
v0.7.0
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Learn about vigilant mode.

This is a major update to AgML that includes a new agml.models API, new datasets, new tools, and a suite of bugfixes and improvements across the board.

Major Changes

  • The agml.models.Detector API has been introduced. This API wraps the Ultralytics YOLO11 model (see ultralytics for more information) and provides an easy-to-use interface for training and inference with state-of-the-art object detection models.
    • Use agml.models.Detector.train(loader, 'yolo_model') to quickly train an AgML dataset using your choice of YOLO model - all right within the AgML API.
    • Use agml.models.Detector.load('model_name') to load a trained model easily, stored directly within the AgML local model repository.
    • Use agml.models.Detector.load_benchmark('dataset_name', 'yolo_name') to access a pre-trained agricultural object detection model from a set of available pretrained benchmarks.
    • Note: If you want fine-grained transfer learning and finetuning capabilities, use the standard agml.models.DetectionModel API, but the agml.models.Detector API is a much more user-friendly quick-start for quick training and inference.
  • We have added four new datasets:
    • tomato_ripeness_detection: Object Detection
    • corn_maize_leaf_disease: Image Classification
    • tomato_leaf_disease: Image Classification
    • vine_virus_photo_dataset: Image Classification
  • A new export tool has been added agml.data.extensions.restructure_cvat_annotations, that automatically reformats the exported COCO annotations from the CVAT tool into the format used in AgML. This increases AgML's interoperability with existing machine learning frameworks.

Major Bugfixes

  • Helios compilation and execution is now fixed and works for Windows systems - Windows users can now use the agml.synthetic API to generate their own simulated data.

Other Changes and Improvements

  • The documentation for AgML datasets has been improved to show more representative samples, and datasets without documentation now have theirs added.
  • Improvements have been made to the agml.data.ImageLoader, and there is now a new method agml.data.AgMLDataLoader.take_images() which enables you to extract just the images from an AgMLDataLoader, useful for inference and other image-only applications.
  • A bug which prevented saving and loading splits for object detection has been fixed: you can now load and save object detection data splits successfully.
  • Some small errors with visualization have been fixed, including orientation and number of images in certain methods.
  • The agml.data.tools.convert_bbox_format tool now has new formats xyxy and yxyx which wrap longer strings, for ease of use.
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