From d7fc42a5ba81cc9fd20f0d905c67a82ec9f73255 Mon Sep 17 00:00:00 2001
From: benedict-96 <55493704+benedict-96@users.noreply.github.com>
Date: Tue, 5 Dec 2023 11:02:20 +0100
Subject: [PATCH] Update README.md

Now using urls for the image files
---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 65025009e..d2ce1105a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <p align="center">
-    <img width="700px" src="logo.png#gh-light-mode-only"/>
-    <img width="700px" src="logo_dark.png#gh-dark-mode-only"/>
+    <img width="700px" src="https://github.com/JuliaGNI/GeometricMachineLearning.jl/assets/55493704/8d6d1410-b857-4e0f-8609-50e43be9a268#gh-light-mode-only"/>
+    <img width="700px" src="https://github.com/JuliaGNI/GeometricMachineLearning.jl/assets/55493704/014929d1-2297-4b2c-9359-58cadbb03a0e#gh-dark-mode-only"/>
 </p>
 
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagni.github.io/GeometricMachineLearning.jl/stable)
@@ -55,4 +55,4 @@ plot(trajectory_to_plot)
 The optimization of the first layer is done on the Stiefel Manifold $St(n, N)$, and the optimizer used is the manifold version of Adam (see (Brantner, 2023)).
 
 ## References
-- Brantner B. Generalizing Adam To Manifolds For Efficiently Training Transformers[J]. arXiv preprint arXiv:2305.16901, 2023.
\ No newline at end of file
+- Brantner B. Generalizing Adam To Manifolds For Efficiently Training Transformers[J]. arXiv preprint arXiv:2305.16901, 2023.