From 618b29058e5a12be044b3ad819142902acf892fc Mon Sep 17 00:00:00 2001
From: Yan Lin <github@yanlincs.com>
Date: Mon, 2 Feb 2026 09:05:20 +0100
Subject: [PATCH] fix over-width image

---
 content/dl4traj/end-to-end/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/dl4traj/end-to-end/index.md b/content/dl4traj/end-to-end/index.md
index 4105a81..933d907 100644
--- a/content/dl4traj/end-to-end/index.md
+++ b/content/dl4traj/end-to-end/index.md
@@ -7,7 +7,7 @@ description = ""
 End-to-end learning means training a model to perform a task from input to output, supervising only on how the output aligns with the task's ground truth.
 End-to-end is typically the most straightforward option for building a deep learning method for a certain task, and that also applies to most tasks related to spatiotemporal trajectories.
 
-<img src="end-to-end.webp" alt="end-to-end" style="max-width: 500px;">
+<img src="end-to-end.webp" alt="end-to-end" style="max-width: min(500px, 100%);">
 
 > Illustration of end-to-end learning of spatiotemporal trajectories.