update readme

Author: franckma31

README.md

@@ -1,8 +1,8 @@
 <div align="center">
         <picture>
-                <source media="(prefers-color-scheme: dark)" srcset="./docs/assets/banner_light_deellip.png">
-                <source media="(prefers-color-scheme: light)" srcset="./docs/assets/banner_dark_deellip.png">
-                <img alt="Library Banner" src="./docs/assets/banner_dark_deellip.png">
+                <source media="(prefers-color-scheme: dark)" srcset="./docs/assets/banner_dark_deellip.png">
+                <source media="(prefers-color-scheme: light)" srcset="./docs/assets/banner_light_deellip.png">
+                <img alt="DEEL-LIP Banner" src="./docs/assets/banner_light_deellip.png">
         </picture>
 </div>
 <br>
@@ -123,8 +123,8 @@ More from the DEEL project:
 
 - [Xplique](https://github.com/deel-ai/xplique) a Python library exclusively dedicated to explaining neural networks.
 - [Influenciae](https://github.com/deel-ai/influenciae) Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
-- [deel-torchlip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
-- [oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
+- [deel-TorchLip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
+- [Oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
 - [DEEL White paper](https://arxiv.org/abs/2103.10529) a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.
 
 ## 🙏 Acknowledgments

docs/index.md

@@ -1,6 +1,6 @@
 <div align="center">
-    <img src="./assets/banner_light_deellip.png#only-dark" width="75%" alt="DEEL-LIP" align="center" />
-    <img src="./assets/banner_dark_deellip.png#only-light" width="75%" alt="DEEL-LIP" align="center" />
+    <img src="./assets/banner_dark_deellip.png#only-dark" width="75%" alt="DEEL-LIP" align="center" />
+    <img src="./assets/banner_light_deellip.png#only-light" width="75%" alt="DEEL-LIP" align="center" />
 </div>
 <br>
 
@@ -112,8 +112,8 @@ More from the DEEL project:
 
 - [Xplique](https://github.com/deel-ai/xplique) a Python library exclusively dedicated to explaining neural networks.
 - [Influenciae](https://github.com/deel-ai/influenciae) Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
-- [deel-torchlip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
-- [oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
+- [deel-TorchLip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
+- [Oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
 - [DEEL White paper](https://arxiv.org/abs/2103.10529) a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.
 
 ## 🙏 Acknowledgments