Project-AgML/AgML
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
AgML provides framework-agnostic data handling with native support for TensorFlow and PyTorch, enabling seamless export to `tf.data.Dataset` or `torch.utils.data.DataLoader` formats. The library features a chainable preprocessing pipeline with integrated augmentation (via Albumentations), masking transformations, and train/val/test splitting, alongside built-in model training utilities for standard architectures like EfficientDet. It also includes visualization tools tailored to agricultural annotation types (overlaid masks, side-by-side comparisons) and exposes 20+ curated datasets spanning classification, detection, segmentation, and regression tasks across global agricultural contexts.
265 stars.
Stars
265
Forks
41
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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