Update README.md

README.md

@@ -93,14 +93,6 @@ Classes list (in same order as class id): 0: "backhoe_loader", 1: "bicycle", 2:
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-<h2>SAGA</h2>
-<div align="center">
-  <img src="/images/SAGA.png" alt="SAGA" style="width:60%;">
-  <p style="font-size:2px">
-    Illustration of SAGA augmentation. The process involves extracting objects from the image, converting them to grayscale, and reintegrating them into the original image while preserving background color information.
-  </p>
-</div>
-
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 <h2>Qualitative Comparison</h2>
 <div align="center">
@@ -135,29 +127,6 @@ python inst_gry.py --coco_json_file /path/to/coco/json --image_directory /path/t
 ```
 
 
-<table>
-  <tr>
-    <th><h2>IndraEye Dataset</h2></th>
-    <th><h2>Qualitative Comparison</h2></th>
-  <tr>
-    <td align="center">
-      <img src="/images/eo_ir.jpg" alt="Qualitative Comparison" style="width: 100%;">
-      <p style="font-size:2px">
-        IndraEye RGB-IR samples. (a,b): High scale-variation medium altitude with minimal slant-angle during day time.
-        (c,d): High scale-variation medium altitude with minimal slant-angle during night time.
-        (e,f): High altitude with lesser slant-angle covering a large area.
-        (g-h): Mid altitude with high scale variations.
-      </p>
-    </td>
-    <td align="center">
-      <img src="/images/cmt_pred.png" alt="SAGA" style="height: 80%; width: 80%;">
-      <p style="font-size:2px">
-    Output predictions to highlight the importance of the SAGA augmentation on CMT algorithm. (a) and (c) shows the increase in false positives while using vanilla CMT. Meanwhile (b) and (d) shows the reduction in false positives when using SAGA with CMT, showcasing its effectiveness.
-  </p>
-    </td>
-  </tr>
-</table>
-
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