Description:

agml 0.6.1

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Overview
AgML is a comprehensive library for agricultural machine learning. Currently, AgML provides
access to a wealth of public agricultural datasets for common agricultural deep learning tasks. In the future, AgML will provide ag-specific ML functionality related to data, training, and evaluation. Here's a conceptual diagram of the overall framework.



AgML supports both the TensorFlow and PyTorch machine learning frameworks.
Installation
To install the latest release of AgML, run the following command:
pip install agml

Quick Start
AgML is designed for easy usage of agricultural data in a variety of formats. You can start off by using the AgMLDataLoader to
download and load a dataset into a container:
import agml

loader = agml.data.AgMLDataLoader('apple_flower_segmentation')

You can then use the in-built processing methods to get the loader ready for your training and evaluation pipelines. This includes, but
is not limited to, batching data, shuffling data, splitting data into training, validation, and test sets, and applying transforms.
import albumentations as A

# Batch the dataset into collections of 8 pieces of data:
loader.batch(8)

# Shuffle the data:
loader.shuffle()

# Apply transforms to the input images and output annotation masks:
loader.mask_to_channel_basis()
loader.transform(
transform = A.RandomContrast(),
dual_transform = A.Compose([A.RandomRotate90()])
)

# Split the data into train/val/test sets.
loader.split(train = 0.8, val = 0.1, test = 0.1)

The split datasets can be accessed using loader.train_data, loader.val_data, and loader.test_data. Any further processing applied to the
main loader will be applied to the split datasets, until the split attributes are accessed, at which point you need to apply processing independently
to each of the loaders. You can also turn toggle processing on and off using the loader.eval(), loader.reset_preprocessing(), and loader.disable_preprocessing()
methods.
You can visualize data using the agml.viz module, which supports multiple different types of visualization for different data types:
# Disable processing and batching for the test data:
test_ds = loader.test_data
test_ds.batch(None)
test_ds.reset_prepreprocessing()

# Visualize the image and mask side-by-side:
agml.viz.visualize_image_and_mask(test_ds[0])

# Visualize the mask overlaid onto the image:
agml.viz.visualize_overlaid_masks(test_ds[0])

AgML supports both the TensorFlow and PyTorch libraries as backends, and provides functionality to export your loaders to native TensorFlow and PyTorch
formats when you want to use them in a training pipeline. This includes both exporting the AgMLDataLoader to a tf.data.Dataset or torch.utils.data.DataLoader,
but also internally converting data within the AgMLDataLoader itself, enabling access to its core functionality.
# Export the loader as a `tf.data.Dataset`:
train_ds = loader.train_data.export_tensorflow()

# Convert to PyTorch tensors without exporting.
train_ds = loader.train_data
train_ds.as_torch_dataset()

You're now ready to use AgML for training your own models!
Public Dataset Listing



Dataset
Task
Number of Images




bean_disease_uganda
Image Classification
1295


carrot_weeds_germany
Semantic Segmentation
60


plant_seedlings_aarhus
Image Classification
5539


soybean_weed_uav_brazil
Image Classification
15336


sugarcane_damage_usa
Image Classification
153


crop_weeds_greece
Image Classification
508


sugarbeet_weed_segmentation
Semantic Segmentation
1931


rangeland_weeds_australia
Image Classification
17509


fruit_detection_worldwide
Object Detection
565


leaf_counting_denmark
Image Classification
9372


apple_detection_usa
Object Detection
2290


mango_detection_australia
Object Detection
1730


apple_flower_segmentation
Semantic Segmentation
148


apple_segmentation_minnesota
Semantic Segmentation
670


rice_seedling_segmentation
Semantic Segmentation
224


plant_village_classification
Image Classification
55448


autonomous_greenhouse_regression
Image Regression
389


grape_detection_syntheticday
Object Detection
448


grape_detection_californiaday
Object Detection
126


grape_detection_californianight
Object Detection
150


guava_disease_pakistan
Image Classification
306


apple_detection_spain
Object Detection
967


apple_detection_drone_brazil
Object Detection
689


plant_doc_classification
Image Classification
2598


plant_doc_detection
Object Detection
2598


wheat_head_counting
Object Detection
6512


peachpear_flower_segmentation
Semantic Segmentation
42


red_grapes_and_leaves_segmentation
Semantic Segmentation
258


white_grapes_and_leaves_segmentation
Semantic Segmentation
273


ghai_romaine_detection
Object Detection
500


ghai_green_cabbage_detection
Object Detection
500


ghai_iceberg_lettuce_detection
Object Detection
500


riseholme_strawberry_classification_2021
Image Classification
3520


ghai_broccoli_detection
Object Detection
500


bean_synthetic_earlygrowth_aerial
Semantic Segmentation
2500


ghai_strawberry_fruit_detection
Object Detection
500


vegann_multicrop_presence_segmentation
Semantic Segmentation
3775



Usage Information
Using Public Agricultural Data
AgML aims to provide easy access to a range of existing public agricultural datasets The core of AgML's public data pipeline is
AgMLDataLoader. You can use the AgMLDataLoader or agml.data.download_public_dataset() to download
the dataset locally from which point it will be automatically loaded from the disk on future runs.
From this point, the data within the loader can be split into train/val/test sets, batched, have augmentations and transforms
applied, and be converted into a training-ready dataset (including batching, tensor conversion, and image formatting).
To see the various ways in which you can use AgML datasets in your training pipelines, check out
the example notebook.
Annotation Formats
A core aim of AgML is to provide datasets in a standardized format, enabling the synthesizing of multiple datasets
into a single training pipeline. To this end, we provide annotations in the following formats:

Image Classification: Image-To-Label-Number
Object Detection: COCO JSON
Semantic Segmentation: Dense Pixel-Wise

Contributions
We welcome contributions! If you would like to contribute a new feature, fix an issue that you've noticed, or even just mention
a bug or feature that you would like to see implemented, please don't hesitate to use the Issues tab to bring it to our attention.
See the contributing guidelines for more information.
Funding
This project is partly funded by the National AI Institute for Food Systems (AIFS).

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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