Update README.md

README.md

@@ -150,7 +150,7 @@ Get hands-on with **Core-JEPA**. Select a tutorial below to open it directly in
       <td>Load Core-JEPA weights, visualize embeddings via <strong>PCA</strong>, and perform a distribution analysis to verify Gaussian Isotropy.</td>
       <td>
         <a href="https://colab.research.google.com/drive/1tVd0bICVPq28jxEMAw5M1pNJ852BHBP_">
-          <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+          <img src="https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252" alt="Open In Colab"/>
         </a>
       </td>
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@@ -159,7 +159,7 @@ Get hands-on with **Core-JEPA**. Select a tutorial below to open it directly in
       <td>Reproduce results on downstream tasks. Trains a <strong>Linear Probe</strong> on the AID dataset to verify the >96% accuracy against DINOv3.</td>
       <td>
         <a href="https://colab.research.google.com/drive/1HUdMoCMJthp2Ge9E67WrZ0FRyQwZR67h">
-          <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+          <img src="https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252" alt="Open In Colab"/>
         </a>
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@@ -168,7 +168,7 @@ Get hands-on with **Core-JEPA**. Select a tutorial below to open it directly in
       <td>Implementation of the original <strong>LeJEPA</strong> paper recipe. Focuses exclusively on <strong>Global Loss</strong> minimization using SIGReg.</td>
       <td>
         <a href="https://colab.research.google.com/drive/1dD1LTy_uDDNzy9eRnRE4hJysd_udyvlT">
-          <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+          <img src="https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252" alt="Open In Colab"/>
         </a>
       </td>
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@@ -177,7 +177,7 @@ Get hands-on with **Core-JEPA**. Select a tutorial below to open it directly in
       <td>Our custom <strong>Core-JEPA</strong> recipe. Extends LeJEPA by adding <strong>Local Loss</strong> (on all tokens) and <strong>Gram Anchoring</strong> for dense feature learning.</td>
       <td>
         <a href="https://colab.research.google.com/drive/18H0rzLktNQsF8tZbIiQX90q_vS9lV7Di">
-          <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+          <img src="https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252" alt="Open In Colab"/>
         </a>
       </td>
     </tr>