AgML 0.7.2

This is a relatively major release that introduces a significant number of new datasets into AgML, alongside various bugfixes and improvements.

New Datasets

The following new datasets have been introduced: embrapa_wgisd_grape_detection, growliflower_cauliflower_segmentation, strawberry_detection_2023, strawberry_detection_2022, almond_harvest_2021, almond_bloom_2023, gemini_flower_detection_2022, gemini_leaf_detection_2022, gemini_pod_detection_2022, gemini_plant_detection_2022, paddy_disease_classification, onion_leaf_classification, chilli_leaf_classification, orange_leaf_disease_classification, papaya_leaf_disease_classification, blackgram_plant_leaf_disease_classification, arabica_coffee_leaf_disease_classification, banana_leaf_disease_classification, coconut_tree_disease_classification, rice_leaf_disease_classification, tea_leaf_disease_classification, betel_leaf_disease_classification, java_plum_leaf_disease_classification, sunflower_disease_classification, and cucumber_disease_classification. This is the largest individual release to-date of datasets. Specifically, 10+ datasets designed for disease classification have been introduced, alongside a variety of publicly-sourced object detection datasets.

Documentation

New documentation has been introduced for AgML. At the moment, it provides an interface for inspecting data online with ease, and will expand to include more information about library usage in general. Check out the documentation at: https://project-agml.github.io/AgML/index.html.

Major Updates

• The agml.synthetic API has been improved and is now tested to work on Windows, in addition to Mac and Linux. Breaking changes that had been introduced into Helios have now been integrated into the AgML API as well.
• The Raditation API that is introduced in Helios, for more accurate and efficient synthetic data generation, has also been incorporated into AgML in an early stage.

Quality Changes

• New tools for testing and uploading datasets, as well as version management on PyPi have been introduced.
• Code quality has been improved throughout the library, improving readability.

More Detailed Information

• Dev by @amogh7joshi in #58
• Add Radiation Project by @smbanx in #59
• pyproject_toml and use uv to sync deps. by @lalmei in #60
• feat/tests by @lalmei in #61
• Add format and linter by @lalmei in #62
• add documentaiton config init by @lalmei in #63
• release draft fix by @lalmei in #64
• docs:hot-fix by @lalmei in #65
• fix lesser filter by @lalmei in #67
• Feat/config test by @lalmei in #68
• New Datasets [Feb 2025] by @naitikjain3071 in #70
• AgML 0.7.1 Changes by @amogh7joshi in #71

New Contributors

• @naitikjain3071 made their first contribution in #70

Full Changelog: v0.7.0...v0.7.2

AgML 0.7.0

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.

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.

AgML 0.6.1

This is a small update.

• A new dataset vegann_multicrop_presence_segmentation has been added: this is the AgML-incorporation of the VegAnn dataset.
• Fixed an issue with the class names in the rangeland_weeds_australia dataset: the awry "no_weeds" class has been removed and the dataset now follows the original specification of the DeepWeeds dataset.
• A bug that prevented the storage/loading of custom splits has been fixed.
• A bug that cut off text when visualizing image classification data samples has been fixed.

Full Changelog: v0.6.0...v0.6.1

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

AgML 0.5.2

New Features and Bugfixes

• Added the ability to save and load data splits for individual datasets using loader.save_split and loader.load_split.
• Added the ability to load multiple datasets using a wildcard name instead of passing each individual dataset in a list (e.g., to load all of the apple detection datasets, use apple_detection*).
• Use agml.models.get_benchmark to get benchmark information for a certain model trained on a certain dataset.
• Fixed bugs with mAP calculation during training.
• Fixed bugs with image preprocessing for object detection and visualization of object detection predictions.

Full Changelog: v0.5.1...v0.5.2

AgML 0.5.1

Main Changes

• Added a new dataset, ghai_strawberry_fruit_detection.
• Added a new method agml.io.read_image for convenience.
• Added the ability to generate both RGB & LiDAR data using the option agml.synthetic.SimulationType.Both.

Bugfixes and Updates

• You can now instantiate a collection of synthetic datasets by passing a list of them to the initialization of an AgMLDataLoader.
• A bug which prevented the visualization of predictions when using a SegmentationModel has now been patched.

Full Changelog: v0.5.0...v0.5.1

AgML 0.5.0

This release introduces synthetic LiDAR data generation through Helios, a rework of the agml.viz module with better functionality, and a number of new available datasets, amongst other features.

Major Changes

• The agml.io module has been added, with a few convenience functions for working with file and directory structures.
  • Currently available functions include get_file_list and get_dir_list which also work with nested structures, as well as recursive_dirname, parent_path, and random_file.

agml.data

• Three new datasets have been introduced:
  • Object Detection: ghai_iceberg_lettuce_detection, ghai_broccoli_detection
  • Image Classification: riseholme_strawberry_classification_2021
• The agml.data.ImageLoader has been added, which is essentially a simple loader designed only for images.
  • Enables loading images from a nested directory structure.
  • Enables easy resizing and transforms of the loaded images.
• The AgMLDataLoader now has a new method show_sample which can be used to visualize samples from the dataset directly from the loader.

agml.synthetic

• LiDAR Data Generation: You can generate LiDAR-based synthetic data now, using opt.simulation_type = agml.synthetic.SimulationType.LiDAR.
• You can recompile Helios with LiDAR enabled, as well as in parallel (for Linux and MacOS systems) using recompile_helios(lidar_enabled = True) and recompile_helios(parallel = True) [note that parallel compilation is enabled by default].
• A new loader agml.synthetic.LiDARLoader has been added, which can be used to load point clouds from a generated directory in the same format as ImageLoader, and can be used to get point clouds and visualize them.

agml.viz

• The entire agml.viz module has been reworked with new methods and a functional visualization backend which enables both cv2 and matplotlib displays, depending on what the user desires.
• The following is a mapping of the old functions to new functions:
  • visualize_images_and_labels -> show_images_and_labels
  • output_to_mask -> convert_mask_to_colored_image
  • overlay_segmentation_masks -> annotate_semantic_segmentation
  • visualize_image_and_mask -> show_image_and_mask
  • visualize_overlaid_masks -> show_image_and_overlaid_mask
  • visualize_image_mask_and_predicted -> show_semantic_segmentation_truth_and_prediction
  • annotate_bboxes_on_image -> annotate_object_detection
  • visualize_image_and_boxes -> show_image_and_boxes
  • visualize_real_and_predicted_bboxes -> show_object_detection_truth_and_prediction
  • visualize_images -> show_images
• To swap between viz backends, use get_viz_backend and set_viz_backend.
• In order to simply display an image, you can use display_image.
• To visualize a point cloud (either in Open3D if installed, or matplotlib), use show_point_cloud.

Other Changes

• Major Change for New Users: torch, torchvision, and other related modeling packages for the agml.models package are no longer distributed with the AgML requirements -- you must install these on your own if you want to use them.

Minor/Functional Changes + Bugfixes

• Fixed backend swapping between TensorFlow and PyTorch when using clashing transforms.
• Added the ability to run prediction with a classification and a segmentation model without normalizing the input image using model.predict(..., normalize = False).
• Images no longer auto-show as matplotlib figures when using agml.viz.show_* methods, instead they are returned as image arrays and can be displayed in a desired format.
• Improved access and setting of information in the backend config.json file, so information is not accidentally overwritten.

Full Changelog: v0.4.7...v0.5.0

AgML 0.4.7

Main Changes

• Introduced two new public object detection datasets from the Western Growers Global Harvest Automation Initiative (https://github.com/AxisAg/GHAIDatasets): ghai_romaine_detection, and ghai_green_cabbage_detection.
• Added the ability to generate multiple different types of annotations when generating synthetic datasets with Helios.

Other Changes

• Fixed errors with different visualization utilities as well as synthetic data annotation conversions.

Full Changelog: v0.4.6...v0.4.7

AgML 0.4.6

Main Changes

• You can now manually generate synthetic data with Helios using your own C++ generation file and CMake parameters/XML style file, using the method agml.synthetic.manual_generate_data. This allows for a greater deal of customizability and control over how Helios is compiled, as well as which methods are being used.
• A convenience method agml.viz.show_sample has been added which enables quick visualization of an image and its annotations: this works for all types of annotations, simply pass a loader as the argument.

Other Improvements

• If you want to overwrite files when generating synthetic data with the HeliosDataGenerator, you can now pass the argument clear_existing_files = True to the generate method.
• You can now compile Helios using the standard API in both the 'Release' and 'Debug' modes.

Read the Full Changelog Here.