.....

app.py

@@ -8,10 +8,10 @@ import tensorflow as tf
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "mattmdjaga/segformer_b2_clothes"
+    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
 )
 model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "mattmdjaga/segformer_b2_clothes"
+    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
 )
 
 def ade_palette():
@@ -35,6 +35,7 @@ def ade_palette():
         [64, 64, 0],  # Class 15
         [64, 0, 64],  # Class 16
         [0, 64, 64],  # Class 17
+        [64, 64, 64],
 
     ]
 
@@ -104,7 +105,7 @@ 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=["22.jpg", "33.jpg"],
                     allow_flagging='never')
 
 

labels.txt

@@ -1,18 +1,19 @@
-Background
-Hat
-Hair
-Sunglasses
-Upper-clothes
-Skirt
-Pants
-Dress
-Belt
-Left-shoe
-Right-shoe
-Face
-Left-leg
-Right-leg
-Left-arm
-Right-arm
-Bag
-Scarf
+road
+sidewalk
+building
+wall
+fence
+pole
+traffic light
+traffic sign
+vegetation
+terrain
+sky
+person
+rider
+car
+truck
+bus
+train
+motorcycle
+bicycle