AgML 0.6.0

This release introduces tools for training custom agricultural machine learning models using AgML.

Main Changes

agml.models

• The agml.models API has been extended with new features for training, namely the run_training method which enables quick training of image classification, semantic segmentation, and object detection models.
  • Simply instantiate a model with your number of classes, build an AgMLDataLoader with your data preprocessing, and pass it to the run_training method alongside other training hyperparameters to train a model.
  • Choose your level of customizability: for newer users, options like optimizers, loss, and other hyperparameters are auto-selected, but for experienced users, you can go as far as extending the training_step, validation_step, and other arguments for greater customizability over training.

Other Changes and Bugfixes

• A major bug which prevented recompliation of Helios without LiDAR has been fixed, enabling users to switch between using LiDAR-compiled Helios and standard Helios.
• A bug which caused Helios installation on basic inspection is now patched. Helios will no longer auto-install unless the agml.synthetic module is actively used for data generation.
• You can now correctly display image samples when using agml.viz.show_sample with the image_only option.
• Bugfixes have been done for agml.models.metrics.Accuracy and agml.models.metrics.MeanAveragePrecision to ensure that they work with training.

Read the Full Changelog: v0.5.2...v0.6.0