Initial commit.

app.py

@@ -173,8 +173,12 @@ def predict(input_img):
 title = "Drive&Segment"
 description = 'Gradio Demo accompanying paper "Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation"\nBecause of the CPU-only inference, it might take up to 20s for large images.\nRight now, I use the Segmenter model trained on nuScenes and with 256x256 patches (for the sake of speed).'
 # article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
-examples = ['examples/img5.jpeg', 'examples/100.jpeg', 'examples/39076.jpeg', 'examples/img1.jpg', 'examples/snow1.jpg']
-examples += ['examples/cs{}.jpg'.format(i) for i in range(1, 6)]
+examples = [ #'examples/img5.jpeg',
+            'examples/100.jpeg',
+            'examples/39076.jpeg',
+            'examples/img1.jpg',
+            'examples/snow1.jpg']
+examples += ['examples/cs{}.jpg'.format(i) for i in range(2, 5)]
 
 # predict(examples[0])