Update README.md with new name

README.md

@@ -27,7 +27,7 @@ pipeline_tag: keypoint-detection
 
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-<h1 style="color: #9334E9;">⚡️ SuperPoint Pruned</h1>
+<h1 style="color: #9334E9;">⚡️ PrunaSuperPoint</h1>
 
 <h2>TensorRT SuperPoint on Jetson Orin Nano</h2>
 
@@ -41,7 +41,7 @@ pipeline_tag: keypoint-detection
 
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-**SuperPoint Pruned is an optimization of the SuperPoint (SP) keypoint detection model. Based on [rpautrat/SuperPoint](https://github.com/rpautrat/SuperPoint) by Rémi Pautrat and Paul-Edouard Sarlin. Optimized for the TensorRT runtime on Jetson Orin Nano devices, but also applicable to other runtime backends. The ideas used can be applied to other similar network architectures.**
+**PrunaSuperPoint is an optimization of the SuperPoint (SP) keypoint detection model. Based on [rpautrat/SuperPoint](https://github.com/rpautrat/SuperPoint) by Rémi Pautrat and Paul-Edouard Sarlin. Optimized for the TensorRT runtime on Jetson Orin Nano devices, but also applicable to other runtime backends. The ideas used can be applied to other similar network architectures.**
 
 This work was done in collaboration with CEA ([Aidge](https://eclipse.dev/aidge/)) for the [DeepGreen](https://deepgreen.ai/) project. You can also find a pruning tutorial for SuperPoint using Aidge operators in the [Aidge codebase](https://gitlab.eclipse.org/eclipse/aidge/aidge/-/tree/main/examples/tutorials/SuperPoint_pruning_tutorial).
 
@@ -169,7 +169,7 @@ This repository is licensed under the **Apache License 2.0**.
 
 ## What's next?
 
-- Experiment with SuperPoint Pruned for computer vision, drone, and edge-device workloads
+- Experiment with PrunaSuperPoint for computer vision, drone, and edge-device workloads
 - Compress your own models with [Pruna](https://github.com/PrunaAI/pruna) and give us a ⭐️
 
 ## Annex: Run your own distillation and compare inference time