PyPI 官网下载 | agml-0.13.tar.gz

# AgML 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. ## Installation To install the latest release of AgML, run the following command: ```shell pip install agml ``` ## Getting Started AgML aims to provide seamless access to resources for users of all levels. The core of AgML's public data pipeline is [`AgMLDataLoader`](/agml/data/loader.py). Simply running the following line of code: ```python loader = AgMLDataLoader('<dataset_name_here>') ``` will download the dataset locally from which point it will be automatically loaded from the disk on future runs. For high-level users who just want the dataset information, accessing the raw metadata is as easy as ```python dataset = loader.export_contents() ``` On the other hand, users who want to integrate the loader into their existing pipelines can use a number of methods can use a number of methods to process and export their data, including applying transforms, batching and splitting the data, and even exporting to PyTorch DataLoaders or TensorFlow Dataset pipelines. For more detailed information about the API, see [insert documentation link here](). ### 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](https://cocodataset.org/#format-data) - **Semantic Segmentation**: Dense Pixel-Wise ## Optional We aim to provide additional datasets for different deep learning tasks in the future. ## Vision AgML aims to be an end-to-end resource encompassing all facets of agricultural machine learning. ```text Include a nicely-formatted graphic of the slide that Mason showed in the first lab meeting showing the vision for AgML? ``` <!-- INTERNAL NOTE: As new releases of AgML are published, this README is going to change significantly. E.g., right now the 'installation' section just discusses `pip install agml`, but that will evolve to discussing CUDA/dev versions or other features as we continue to add features to the library. So, this is just the first template as we introduce the first releases. -->