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 # UniPercept
 
+This package contains a collection of libraries for rapid testing and development in computer vision research settings. 
+
+Note that this is explicitly not a 'framework', but rather a collection of common utlities and boilerplate code. 
+The individual libraries can be individually used and combined, without requiring a large shift in paradigm or major cross-dependencies.
+
+Additionally, `unipercept` includes Hydra-based configurataion system and CLI to speed up common research tasks.
+
 ## Installation
 
 This package requires at least Python 3.11 and PyTorch 2.1. Once you have created an environment with these 
@@ -84,21 +91,6 @@ conda env config vars set CXX=$(which g++)
 ```
 
 Then, reload the environment and install `unipercept` as usual.
-
-## Acknowledgements
-
-We would like to express our gratitude to the developers of the following open-source projects, which have significantly contributed to the success of our work:
-
-- [PyTorch](https://github.com/pytorch/pytorch): An open-source machine learning framework that accelerates the path from research prototyping to production deployment.
-- [Detectron2](https://github.com/facebookresearch/detectron2): A platform for object detection and segmentation built on PyTorch. We liberally use the packages and code from this project.
-- [PyTorch3D](https://github.com/facebookresearch/pytorch3d): A library on which we base our camera projection from 2D to 3D space.
-- [Panoptic FCN](https://github.com/DdeGeus/PanopticFCN_cityscapes): An implementation of the Panoptic FCN method for panoptic segmentation tasks.
-- [ViP-DeepLab](https://github.com/google-research/deeplab2/blob/main/g3doc/projects/vip_deeplab.md): The baseline implementation for the depth-aware video panoptic segmentation task.
-- [Panoptic Depth](https://github.com/NaiyuGao/PanopticDepth): A repository that implements the instance (de)normalization procedure that significantly improves depth estimation for _things_.
-
-The Unified Perception implementation contains extracts of the above repositories that have been edited to suit the specific needs of this project.
-Whenever possible, the original libraries are used instead.
-
 ## License
 
 This repository is released under the MIT License. For more information, please refer to the LICENSE file.