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Overview

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Want to join the AI Institute for Food Systems team and help lead AgML development?​

We're looking to hire a postdoc with both Python library development and ML experience. Send your resume and GitHub profile link to jmearles@ucdavis.edu!

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.

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 datasets are hosted on the Hugging Face Hub under the Project-AgML organization. You can start off by using the HuggingFaceDataLoader to download and load a dataset directly into a native Hugging Face DatasetDict:

from agml.data import HuggingFaceDataLoader

# Load a dataset from the Hub
loader = HuggingFaceDataLoader("Project-AgML/apple_flower_segmentation")

# Load a specific config/subset (e.g. an augmented variant)
loader = HuggingFaceDataLoader("Project-AgML/apple_flower_segmentation", config="augmented")

HuggingFaceDataLoader automatically casts image-like columns (image, mask, and image-typed label columns) to the Hugging Face Image type for decoded pixel access.

You can split the dataset into train/val/test sets, with optional stratification across one or more columns:

dataset = loader.split(val_size=0.1, test_size=0.1, stratify_cols="label")
# Returns a DatasetDict with 'train', 'val', and 'test' splits

# Access the underlying DatasetDict at any time
dataset = loader.dataset

For any preprocessing, inference, or training beyond loading and splitting, use the datasets and transformers libraries directly. Since loader.dataset is a native Hugging Face DatasetDict, it works out of the box with datasets' map, filter, and with_transform methods for preprocessing, and with transformers' Trainer, Pipeline, and model classes for training and inference — there's no separate AgML-specific processing API to learn.

from transformers import AutoImageProcessor, AutoModelForImageClassification, Trainer

processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained(
    "google/vit-base-patch16-224",
    num_labels=dataset["train"].features["label"].num_classes,
)

def preprocess(batch):
    batch["pixel_values"] = processor(batch["image"], return_tensors="pt")["pixel_values"]
    return batch

dataset = dataset.with_transform(preprocess)

trainer = Trainer(model=model, train_dataset=dataset["train"], eval_dataset=dataset["val"])
trainer.train()

Public Datasets​

AgML contains a wide variety of public datasets from various locations across the world:

Use the Dataset Search experience for filtering and previews. For programmatic filtering, agml.data.public_data_sources(...) supports task and modality filters.

iNatAg and iNatAg-mini​

AgML provides an API with direct access to iNatAg (and iNatAg-mini), one of the world's largest collections of agricultural images dedicated for the task of image classification. Collectively, this dataset contains over 4 million images along with detailed species classificaations and enables access to a variety of large-scale agricultural machine learning tasks. You can instantiate the iNatAg (or iNatAg-mini, a smaller variant of iNatAg for smaller-scale applications) dataset as follows:

# To select a collection of scientific family names.
loader = agml.data.AgMLDataLoader.from_parent("iNatAg", filters={"family_name": ["...", "..."]})

# To select common names.
loader = agml.data.AgMLDataLoader.from_parent("iNatAg", filters={"common_name": "..."})

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).

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. Datasets on the Hugging Face Hub encode annotations as columns on the underlying Dataset/DatasetDict:

  • Image Classification: a label column of type ClassLabel.
  • Object Detection: an objects column, holding COCO-style bounding boxes (un-normlized [x_min, y_min, width, height]) and corresponding category IDs as a ClassLabel per image.
  • Semantic Segmentation: a mask column, a single-channel (L-mode) image the same size as the corresponding image.

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, or the guide to contributing leaderboard results if you have benchmark results to add.

Funding​

This project is partly funded by the National AI Institute for Food Systems.