Initial commit.

app.py

@@ -12,7 +12,7 @@ from torchvision import transforms
 
 # WEIGHTS = './weights/segmenter.pth
 WEIGHTS = './weights/segmenter_nusc.pth'
-FULL = False
+FULL = True
 ALPHA = 0.5
 
 
@@ -171,8 +171,8 @@ 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>"
+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, it uses the Segmenter model trained on nuScenes and with a simplified inference scheme (for the sake of speed).'
+article = "<p style='text-align: center'><a href='https://vobecant.github.io/DriveAndSegment/' target='_blank'>Project Page</a> | <a href='https://github.com/vobecant/DriveAndSegment' target='_blank'>Github</a></p>"
 examples = [ #'examples/img5.jpeg',
             'examples/100.jpeg',
             'examples/39076.jpeg',
@@ -183,6 +183,7 @@ examples += ['examples/cs{}.jpg'.format(i) for i in range(2, 5)]
 # predict(examples[0])
 
 iface = gr.Interface(predict, inputs=gr.inputs.Image(type='filepath'), title=title, description=description,
+                     article=article,
                      outputs=[gr.outputs.Image(label="Pseudo segmentation", type="pil"),
                               gr.outputs.Image(label="Mapping to Cityscapes", type="pil")],
                      examples=examples)