Add the description of what is the meaning of the dynamic in this model.

README.md

@@ -16,6 +16,9 @@ license: mit
 ---
 <h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
 
+> An arbitrary shape adaptable BiRefNet for general segmentation.  
+> This model was trained on arbitrary shapes (256x256 ~ 2304x2304) and shows great robustness on inputs with any shape.
+
 <div align='center'>
     <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>,&thinsp;
     <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,&thinsp;
@@ -118,9 +121,10 @@ birefnet.half()
 
 def extract_object(birefnet, imagepath):
     # Data settings
-    image_size = (1024, 1024)
+    # image_size = (1024, 1024)
+    # Since this model was trained on arbitrary shapes (256x256 ~ 2304x2304), the resizing is not necessary.
     transform_image = transforms.Compose([
-        transforms.Resize(image_size),
+        # transforms.Resize(image_size),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ])