Update README.md

README.md

@@ -2,33 +2,6 @@
 license: mit
 ---
 
-### Vision Transformer (ViT) with LoRA for Spectrogram Regression
-
----
-### Fine-Tuning Details
-
-| Category              | Specification                                                                                     |
-|-----------------------|---------------------------------------------------------------------------------------------------|
-| **Framework**         | PyTorch                                                                                          |
-| **Architecture**      | Pre-trained Vision Transformer (ViT)                                                             |
-| **Adaptation Method** | LoRA (Low-Rank Adaptation)                                                                        |
-| **Task**             | Regression on time-frequency representations                                                      |
-| **Target Variables**  | 1. Chirp start time (ms)<br>2. Start frequency (kHz)<br>3. End frequency (kHz)                   |
-| **Training Protocol** | • Automatic Mixed Precision (AMP)<br>• Early stopping<br>• Learning Rate scheduling              |
-| **Output**           | Quantitative predictions + optional natural language descriptions                                 |
-
----
-### Resource Details
-
-| Resource | Description | Link |
-|----------|-------------|------|
-| Trained Vision Transformer Model | Access to a pre-trained Vision Transformer model fine-tuned on synthetic spectrograms for chirp localization | [HuggingFace Model Hub](https://huggingface.co/nubahador/Fine_Tuned_Transformer_Model_for_Chirp_Localization/tree/main) |
-| Synthetic Spectrogram Dataset | Download link for 100,000 synthetic spectrograms with corresponding labels for chirp localization | [HuggingFace Dataset Hub](https://huggingface.co/datasets/nubahador/ChirpLoc100K___A_Synthetic_Spectrogram_Dataset_for_Chirp_Localization/tree/main) |
-| PyTorch Implementation | Repository containing the PyTorch code for fine-tuning the Vision Transformer on spectrograms | [Implementation GitHub Repository](https://github.com/nbahador/Train_Spectrogram_Transformer) |
-| Synthetic Chirp Generator | Python package for generating synthetic chirp spectrograms (images with corresponding labels) | [Dataset GitHub Repository](https://github.com/nbahador/chirp_spectrogram_generator) |
-
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-
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     <div style="flex: 1; min-width: 200px; background: white; border-radius: 8px; padding: 15px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
         <h4 style="margin-top: 0; color: #5f6368;">🧑‍💻 Curated by</h4>
@@ -43,6 +16,76 @@ license: mit
         <p>MIT</p>
     </div>
 </div>
+
+<div style="background: #f8f9fa; border-radius: 8px; padding: 20px; margin: 20px 0; border-left: 4px solid #4285f4;">
+<h2 style="margin-top: 0;">🔍 Model Architecture</h2>
+<div style="background: white; border-radius: 8px; padding: 15px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h3 style="margin-top: 0;">Vision Transformer (ViT) with LoRA for Spectrogram Regression</h3>
+    
+    <div style="margin-bottom: 15px;">
+        <h4 style="margin-bottom: 10px;">Fine-Tuning Details</h4>
+        <table style="width: 100%; border-collapse: collapse;">
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee; width: 30%;"><strong>Framework</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">PyTorch</td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Architecture</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">Pre-trained Vision Transformer (ViT)</td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Adaptation Method</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">LoRA (Low-Rank Adaptation)</td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Task</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">Regression on time-frequency representations</td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Target Variables</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">
+                    1. Chirp start time (ms)<br>
+                    2. Start frequency (kHz)<br>
+                    3. End frequency (kHz)
+                </td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Training Protocol</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;">
+                    • Automatic Mixed Precision (AMP)<br>
+                    • Early stopping<br>
+                    • Learning Rate scheduling
+                </td>
+            </tr>
+            <tr>
+                <td style="padding: 8px;"><strong>Output</strong></td>
+                <td style="padding: 8px;">Quantitative predictions + optional natural language descriptions</td>
+            </tr>
+        </table>
+    </div>
+
+    <div>
+        <h4 style="margin-bottom: 10px;">Resource Details</h4>
+        <table style="width: 100%; border-collapse: collapse;">
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee; width: 30%;"><strong>Trained Vision Transformer Model</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><a href="https://huggingface.co/nubahador/Fine_Tuned_Transformer_Model_for_Chirp_Localization/tree/main">HuggingFace Model Hub</a></td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>Synthetic Spectrogram Dataset</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><a href="https://huggingface.co/datasets/nubahador/ChirpLoc100K___A_Synthetic_Spectrogram_Dataset_for_Chirp_Localization/tree/main">HuggingFace Dataset Hub</a></td>
+            </tr>
+            <tr>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><strong>PyTorch Implementation</strong></td>
+                <td style="padding: 8px; border-bottom: 1px solid #eee;"><a href="https://github.com/nbahador/Train_Spectrogram_Transformer">Implementation GitHub Repository</a></td>
+            </tr>
+            <tr>
+                <td style="padding: 8px;"><strong>Synthetic Chirp Generator</strong></td>
+                <td style="padding: 8px;"><a href="https://github.com/nbahador/chirp_spectrogram_generator">Dataset GitHub Repository</a></td>
+            </tr>
+        </table>
+    </div>
+</div>
 </div>
 
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@@ -86,7 +129,7 @@ license: mit
 
 <div style="background: #f8f9fa; border-radius: 8px; padding: 20px; border-left: 4px solid #00bcd4;">
 <h2 style="margin-top: 0;">ℹ️ More Information</h2>
-<p>For more information and generation code, visit the <a href="https://github.com/nbahador/chirp_spectrogram_generator/tree/main">GitHub repository</a>.</p>
+<p>For more information and generation code, visit the <a href="https://github.com/nbahador/Train_Spectrogram_Transformer">GitHub repository</a>.</p>
 
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     <h4 style="margin-bottom: 5px;">Dataset Card Authors</h4>