Metadata-Version: 2.1
Name: agml
Version: 0.1
Summary: A comprehensive library for agricultural deep learning
Home-page: https://github.com/plant-ai-biophysics-lab/AgML
Author: UC Davis Plant AI and Biophysics Lab
Author-email: jmearles@ucdavis.edu
Maintainer: Amogh Joshi
Maintainer-email: amnjoshi@ucdavis.edu
License: UNKNOWN
Description: # 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.
        
        -->
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: MacOS
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: test
