vobecant
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app.py
CHANGED
@@ -174,7 +174,7 @@ title = "Drive&Segment"
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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).'
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# article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
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examples = ['examples/img5.jpeg', 'examples/100.jpeg', 'examples/39076.jpeg', 'examples/img1.jpg', 'examples/snow1.jpg']
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examples += ['examples/cs{}.
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# predict(examples[0])
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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).'
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# article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
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examples = ['examples/img5.jpeg', 'examples/100.jpeg', 'examples/39076.jpeg', 'examples/img1.jpg', 'examples/snow1.jpg']
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examples += ['examples/cs{}.jpg'.format(i) for i in range(1, 6)]
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# predict(examples[0])
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