Update app.py

app.py

@@ -8,33 +8,34 @@ import tensorflow as tf
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
+    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
 )
 model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
+    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
 )
 
 def ade_palette():
     """ADE20K palette that maps each class to RGB values."""
     return [
-        [34, 116, 28],
-        [84, 57, 0],
-        [48, 0, 0],
-        [0, 0, 0],
-        [70, 65, 217],
-        [117, 76, 0],
-        [0, 33, 135],
-        [255, 255, 255],
-        [66, 39, 0],
-        [240, 100, 39],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [256, 236, 197],
-        [51, 51, 51],
-        [206, 114, 61],
+        [204, 87, 92],
+        [112, 185, 212],
+        [45, 189, 106],
+        [234, 123, 67],
+        [78, 56, 123],
+        [210, 32, 89],
+        [90, 180, 56],
+        [155, 102, 200],
+        [33, 147, 176],
+        [255, 183, 76],
+        [67, 123, 89],
+        [190, 60, 45],
+        [134, 112, 200],
+        [56, 45, 189],
+        [200, 56, 123],
+        [87, 92, 204],
+        [120, 56, 123],
+        [45, 78, 123],
+        [156, 200, 56]
     ]
 
 labels_list = []
@@ -103,8 +104,8 @@ def sepia(input_img):
 demo = gr.Interface(fn=sepia,
                     inputs=gr.Image(shape=(400, 600)),
                     outputs=['plot'],
-                    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
+                    examples=["img_1.jpg", "img_2.jpeg", "img_3.jpg", "img_4.jpg", "img_5.png"],
                     allow_flagging='never')
 
 
-demo.launch()
+demo.launch()