Update README.md

README.md

@@ -22,7 +22,7 @@ _**"How could we process images of different modalities, with different number o
 **Impact:** Trained in a self-supervised manner, ChAda-ViT sets new benchmarks in biological image analysis. It not only excels in various biologically relevant tasks but also pioneers in bridging the gap across different assays. Whether it's varying microscopes, channel numbers, or types, ChAda-ViT offers a unified, powerful representation for biological images. This paves the way for enhanced interdisciplinary studies and broadens the horizon for deep learning applications in bioimage-based research.
 
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-  <img width="100%" alt="ChAda-ViT architecture" src=".github/chada_vit.png">
+  <img width="100%" alt="ChAda-ViT architecture" src="docs/chada_vit.png">
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 ## 🗾 Dataset
@@ -32,7 +32,7 @@ The IDRCell100k dataset is a comprehensive collection of biological images, meti
 Dataset available soon...
 
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-  <img width="70%" alt="IDRCell100k dataset samples" src=".github/idrcell100k.png">
+  <img width="70%" alt="IDRCell100k dataset samples" src="docs/idrcell100k.png">
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 ## 📈 Results
@@ -44,21 +44,21 @@ For detailed analyses, comprehensive results, and in-depth discussions, please r
 ChAda-ViT exhibits exceptional performance across a range of classical biological image benchmarks. Its advanced architecture allows for precise and efficient analysis, outperforming existing models in accuracy and computational efficiency. This highlights the model's significant contribution to the field of bioimaging.
 
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-  <img width="50%" alt="Vizualization of attention maps" src=".github/classic_benchmarks.png">
+  <img width="50%" alt="Vizualization of attention maps" src="docs/classic_benchmarks.png">
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 ### Visualization of Attention Maps
 The model's innovative Inter-Channel Attention mechanism is visualized, demonstrating its effectiveness in focusing on crucial features within diverse channel types. These visualizations provide insights into the model's internal processing, revealing how it distinguishes and prioritizes different aspects of biological images.
 
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-  <img width="80%" alt="Vizualization of attention maps" src=".github/attn_viz.png">
+  <img width="80%" alt="Vizualization of attention maps" src="docs/attn_viz.png">
 </div>
 
 ### Single Joint Embedding Space
 ChAda-ViT uniquely embeds images from various modalities into a single, coherent representation space. This feature underscores the model's versatility and its ability to handle images from different microscopes, channel numbers, or types, facilitating a more unified approach in biological image analysis.
 
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-  <img width="60%" alt="Projection into a single joint embedding space" src=".github/single_joint_embedding_space.png">
+  <img width="60%" alt="Projection into a single joint embedding space" src="docs/single_joint_embedding_space.png">
 </div>