Update app.py

app.py

@@ -14,19 +14,24 @@ from PIL import Image
 
 from segment_anything import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
 
-# st.markdown(
-#     """
-# The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. 
-# It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.
-# """
-# )
+st.markdown(
+    """
+# Segment Anything Model (SAM) 
+### A test on remote sensing data
+
+- Paper:[Link](https://scontent-fml2-1.xx.fbcdn.net/v/t39.2365-6/10000000_900554171201033_1602411987825904100_n.pdf?_nc_cat=100&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=Ald4OYhL6hgAX-ZcGmS&_nc_ht=scontent-fml2-1.xx&oh=00_AfDk4FvyiDYeXgflANA2CbdV6HSS8CcJmrvjSfTqsgUmog&oe=643500E7)
+- Github:[https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)
+- Dataset:https://ai.facebook.com/datasets/segment-anything-downloads/(https://ai.facebook.com/datasets/segment-anything-downloads/)
+- Official Demo:[https://segment-anything.com/demo](https://segment-anything.com/demo)
+"""
+)
 
 # suppress server-side GUI windows
 matplotlib.pyplot.switch_backend('Agg') 
 
 # setup models
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-sam = sam_model_registry["vit_b"](checkpoint="./sam_vit_b_01ec64.pth")
+sam = sam_model_registry["vit_b"](checkpoint="./meta-model.pth")
 sam.to(device=device)
 mask_generator = SamAutomaticMaskGenerator(sam)
 predictor = SamPredictor(sam)