AgML 0.2.7

This release introduces two new object detection datasets: plant_doc_detection and wheat_head_counting.

In addition, a number of fixes have been made to existing datasets:

• plant_doc_classification: Class names have been updated.
• fruit_detection_worldwide: Class indexing has been redone from 1 instead of 0 (to fit the object detection standard).
• apple_detection_usa: There was an issue where multiple images had the same ID, this has been fixed so each image has a unique ID.
• rice_seedling_segmentation: Removed the redundant background class and re-indexed from 1.

Read the Full Changelog Here.

AgML 0.2.6

This is a bugfix-oriented release. The biggest new change is the removal of the cotton_seedling_counting dataset.

Bugfixes

1. Fixed an issue regarding splitting datasets using proportions (float values).
2. Updated the number of images in plant_seedlings_aarhus to reflect the actual count.
3. Updated the error message displayed when accessing nonexistent metadata.

AgML 0.2.5

This release adds three new datasets:

• Object Detection: apple_detection_drone_brazil and apple_detection_spain.
• Image Classification: guava_disease_pakistan.

It also fixes a bug which was preventing data serialization and splitting.

AgML 0.2.4

This release adds three new object detection datasets: grape_detection_syntheticday, grape_detection_californiaday, and grape_detection_californianight, as well as general bugfixes.

AgML 0.2.3

This release adds a new dataset, autonomous_greenhouse_regression, as well as general support for image regression datasets.

AgML 0.2.2

This release provides some small updates and bugfixes.

Changes

agml.data

• Added calculated mean and standard deviation for all public datasets.
• Added a new normalize_images() method for the AgMLDataLoader which automatically scales images to the 0-1 range and applies normalization.
• Updated data splitting and fixed a number of bugs including creating copies of the original managers, correctly instantiating new DataObjects, and applying batching.

agml.backend

• Updated an instantiation bug which sometimes occurred when newly installing the module and generating the config.json.

AgML 0.2.1

This release reworks the AgMLDataLoader internals and updates its compatibility with TensorFlow/PyTorch training pipelines.

Changes

agml.data

• Reworks the AgMLDataLoader architecture, removing the task-type-based subclasses and adding internal data management classes.
• The batch, split, and shuffle methods can be used to perform different operations on the data within the AgMLDataLoader itself.
• The transform and resize_images methods can be used to apply transforms to the data or resize the images to a specific size (or auto-resize the images smartly).
  • The new method labels_to_one_hot convert labels to one-hot-vectors for image classification tasks.
• The as_keras_sequence and as_torch_dataset methods (alongside the reset_preprocessing, disable_preprocessing, and eval methods) enable the AgMLDataLoader to be used for training and evaluation modes independently, and directly in TensorFlow/PyTorch pipelines.
• The export_contents method exports the raw data mapping for the user to use outside of agml.
• The export_torch and export_tensorflow methods convert the loader to a torch.utils.data.DataLoader or tf.data.Dataset, respectively.

agml.backend

• Added the capability to set a global path to save datasets, using agml.backend.set_data_save_path. Once run, this will change the path all datasets are downloaded to until it is either reset or changed back.

agml.viz

• Bugfixes and improved visualization for different types of input images (both normalized and unnormalized).