last

app.py

@@ -15,7 +15,7 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
     "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
 )
 
-def ade_palette():
+def palette():
 
     return [
         [204, 87, 92],  # road (Reddish)
@@ -46,7 +46,7 @@ with open(r'labels.txt', 'r') as fp:
     for line in fp:
         labels_list.append(line[:-1])
 
-colormap = np.asarray(ade_palette())
+colormap = np.asarray(palette())
 
 def label_to_color_image(label):
     if label.ndim != 2:
@@ -110,11 +110,11 @@ demo = gr.Interface(fn=sepia,
                     inputs=gr.Image(shape=(564,846)),
                     outputs=['plot'],
                     live=True,
-                    examples=["city1.jpg","city2.jpg","city3.jpg"],
+                    examples=["city1.jpg","city2.jpg","city3.jpg","city4.jpg","city5.jpg"],
                     allow_flagging='never',
                     title="City Image Segmentation Model",
                     theme="huggingfacedark",
-                    description="This model is a high-performance city image segmentation model based on the Segformer architecture provided by NVIDIA. Specifically, the 'segformer-b5' model, trained on the Cityscapes dataset, excels at performing intricate segmentation on high-resolution images of 1024x1024 pixels. It accurately identifies various urban elements such as roads, buildings, pedestrians, providing visually rich segmentation results.\nThis is a machine learning activity project at Kyunggi University.",
+                    description="This model is a high-performance city image segmentation model based on the Segformer architecture provided by NVIDIA. Specifically, the 'segformer-b5' model, trained on the Cityscapes dataset, excels at performing intricate segmentation on high-resolution images of 1024x1024 pixels. It accurately identifies various urban elements such as roads, buildings, pedestrians, providing visually rich segmentation results.This is a machine learning activity project at Kyunggi University.",
 
                     )