AIDE is two things in one: a tool for manually annotating images and a tool for training and running machine (deep) learning models. Those two things are coupled in an active learning loop: the human annotates a few images, the system trains a model, that model is used to make predictions and to select more images for the human to annotate, etc.
More generally, AIDE is a modular Web framework for labeling image datasets with AI assistance. AIDE is configurable for a variety of tasks, but it is particularly intended for ecological applications, such as the acceleration wildlife surveys that use aerial images.
AIDE is primarily developed by Benjamin Kellenberger, supported by the Microsoft AI for Earth program.
- Highlights
- News
- Demo
- Installation and launching AIDE
- AI models in AIDE
- Publications and References
- Contributing
- Powerful: AIDE explicitly integrates humans and AI models in an annotation loop.
- Fast: AIDE has been designed with speed in mind, both in terms of computations and workflow.
- Flexible: The framework allows full customizability, from hyperparameters to models to annotation types to libraries. It provides:
- Support for image classification, point annotations, and bounding boxes (object detection)
- Many deep learning-based AI models and Active Learning criteria built-in
- Interfaces for custom AI models and criteria, using any framework or library you want (see how to write your own model).
- Fully featured: Beyond image labeling and model training, AIDE has management and graphical user/machine performance evaluation tools built-in, right in the web browser, allowing for advanced, manual label quality checks.
- Modular: AIDE is separated into individual modules, each of which can be run on separate machines for scalability. It even supports on-the-fly addition of computational workers for computationally intensive model training!
AIDE v2.1 is out! This includes a new interactive installer for Debian/Ubuntu systems as well as a plethora of bug fixes.
A demo of AIDE v2 can be accessed here.
This demo allows exploring the annotation front-end with a number of example datasets, including:
- Image labels on the Snapshot Serengeti camera traps dataset
- Points on the VGG Penguins dataset
- Bounding boxes on the NOAA Arctic Seals aerial imagery
- Semantic segmentation on the Chesapeake Land Cover satellite imagery
See here.
AIDE ships with a set of built-in models that can be configured and customized:
| Label type | AI model | Model variants / backbones | More info |
|---|---|---|---|
| Image labels | AlexNet | AlexNet | paper |
| DenseNet | DenseNet-161 | paper | |
| MNASNet | MNASNet | paper | |
| MobileNet | MobileNet V2 | paper | |
| ResNet | ResNet-18; ResNet-34; ResNet-50; ResNet-101; ResNet-152 | paper | |
| ResNeXt | ResNeXt-50; ResNeXt-101 | paper | |
| ShuffleNet | ShuffleNet V2 | paper | |
| SqueezeNet | SqueezeNet | paper | |
| VGG | VGG-16 | paper | |
| Wide ResNet | Wide ResNet-50; Wide ResNet-101 | info | |
| Bounding boxes | Faster R-CNN | with ResNet-50 (PASCAL VOC); with ResNet-50 (MS-COCO); with ResNeXt-101 FPN (MS-COCO) | paper, implementation details |
| RetinaNet | with ResNet-50 FPN (MS-COCO); with ResNet-101 FPN (MS-COCO) | paper, implementation details | |
| TridentNet | with ResNet-50; ResNet-101 (MS-COCO) | paper, implementation details | |
| Segmentation masks | DeepLabV3+ | with modified ResNet-101 (Cityscapes) | paper, implementation details |
All models can be configured in various ways through the AI model settings page in the Web browser. They all are pre-trained on ImageNet unless specified otherwise. To use one of the built-in models, simply import the requested one to your project through the Model Marketplace in the Web browser and start training/predicting!
AIDE is fully modular and supports custom AI models, as long as they provide a Python interface and can handle at least one of the different annotation and prediction types appropriately. We greatly welcome contributions and are happy to help in the implementation of your custom models!
See here for instructions on implementing custom models into AIDE.
Please cite the following paper if you use AIDE in your work:
Kellenberger, Benjamin, Devis Tuia, and Dan Morris. "AIDE: Accelerating image‐based ecological surveys with interactive machine learning." Methods in Ecology and Evolution 11(12), 1716-1727. DOI: 10.1111/2041-210X.13489.
@article{kellenberger2020aide,
title={AIDE: Accelerating image-based ecological surveys with interactive machine learning},
author={Kellenberger, Benjamin and Tuia, Devis and Morris, Dan},
journal={Methods in Ecology and Evolution},
volume={11},
number={12},
pages={1716--1727},
year={2020},
publisher={Wiley Online Library}
}If you use AIDE, we would be happy to hear from you! Please send us an E-mail with a little bit of info about your use case; besides getting to know the fellow usership of our software, this also enables us to provide somewhat more tailored support for you if needed. Thank you very much.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.