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https://github.com/SysCV/nutsh/assets/1063562/3e453f9a-df79-42fd-b629-cdc76fe8249d
nutsh
is a platform designed for visual learning through human feedback. With a user-friendly interface and API, it supports a range of visual modalities, diverse human input methods, and learning mechanisms based on human feedback.
The project is currently in its early stages and under active development. Your feedback is highly appreciated.
eval "$(curl -sSL nutsh.ai/install)"
Refer to the quick start documentation for a more comprehensive guide.
https://github.com/SysCV/nutsh/assets/1113875/55925677-57ff-4870-b853-d67c80517eb7
In addition to standard polygon operations, Bézier curves are supported, enabling precise annotations of curved objects, such as tires.
https://github.com/SysCV/nutsh/assets/1113875/594139a8-b5ad-47d7-a87b-01a5eb699ae5
Create masks with pixel-level accuracy, ensuring a perfect fit with no gaps or overlaps.
https://github.com/SysCV/nutsh/assets/1113875/c9977bd7-7700-4f71-97ba-6a2bd2084db5
Especially useful when annotating videos, the platform offers an effortless way to track objects across frames.
https://github.com/SysCV/nutsh/assets/1113875/849b0017-88a9-41a5-b415-bd11475e9e0e
Objects obscured by others, resulting in fragmented representation, can easily be linked to form a cohesive annotation
https://github.com/SysCV/nutsh/assets/1113875/f94b0ac2-4c3b-4513-ac92-b3cb531c41f4
The platform incorporates various shortcuts, streamlining the annotation process for enhanced speed and accuracy.
For polygons sharing a common edge segment, one can be cloned for the other, ensuring a seamless fit without any gaps or overlaps.
https://github.com/SysCV/nutsh/assets/1113875/b13ebac0-921d-4ad4-96fb-9bc33669143c
Given two annotations of the same type on both a start and an end frame, heuristic interpolation can automatically generate all intermediate annotations.
https://github.com/SysCV/nutsh/assets/1113875/6d625d24-b824-4107-9e70-4685c8dc38d9
We offer an API that seamlessly integrates the human labeling interface with deep learning models. Our server facilitates model inference and tuning based on user input. Calculations can be executed on both CPU and GPU platforms.
For instance, our SAM module taps into the Segment Anything Model from Meta AI to enhance segmentation speed and efficiency. While the SAM models are openly accessible to the public, their labeling interface remains proprietary. We also offer advanced features, such as local prediction, to ensure top-notch segmentation results. For a detailed guide, please refer to our documentation.
Leverage deep learning models to perform segmentation tasks across entire images, aiding the segmentation process.
https://github.com/SysCV/nutsh/assets/1113875/c64913d8-6422-40b4-bb4b-8e35f86f9af1
Direct your attention to specific regions of an image and request the model to generate segmentation predictions for that particular section. Such localized predictions often yield more detailed segmentations.
https://github.com/SysCV/nutsh/assets/1113875/b486d6ac-a462-4d6b-aecd-d992055855a9
In addition to utilizing prompts for refining predictions, users can make subsequent adjustments to these predictions. By gathering these modifications, you can train a model that's fine-tuned to your specific needs. Our SAM module comes equipped with features that assist in fine-tuning the SAM decoder seamlessly.
https://github.com/SysCV/nutsh/assets/1113875/2f9c8765-e1bf-4c6d-8e64-91724ac088a3
Nutsh offers a Python SDK, enabling the quick and easy integration of your custom models in Python into the platform. Additionally, our gRPC interfaces are available for a more low-level but flexible approach to integrating your ideas. As an example, check the auto-tracking feature for videos to see how integrated tracking models can enhance your annotation workflows.
https://github.com/SysCV/nutsh/assets/1113875/aa498d08-d7f0-4869-983a-6249d98befdf
Consult the documentation for further information, including:
and more!
If you find the platform useful for your research projects, please cite
@misc{nutsh,
title = {nutsh: A Platform for Visual Learning from Human Feedback},
author = {Xu Han and Fisher Yu},
howpublished = {\url{https://github.com/SysCV/nutsh}},
year = {2023}
}