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	Add description and title again
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        app.py
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         @@ -178,9 +178,11 @@ examples=[ 
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            <b>
         
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            <p style="text-align:center">
         
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            <a href='https://twitter.com/dacl_ai' target='_blank'>Twitter</a><a href='https://x.com/dacl_ai' target='_blank'>/X</a> |
         
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         @@ -214,11 +216,25 @@ with gr.Blocks() as app: 
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              <li>Model: <a href='https://huggingface.co/nvidia/mit-b1' target='_blank'>SegFormer mit-b1</a>, trained on resized 512x512 images for (only) 10 epochs.</li>
         
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              <li>Label description of dacl10k dataset: "A.3. Class descriptions" in <a href='https://arxiv.org/pdf/2309.00460.pdf' target='_blank'>J. Flotzinger, P.J. Rösch, T. Braml: "dacl10k: Benchmark for Semantic Bridge Damage Segmentation".</a></li>
         
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            </ul> 
         
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            """
         
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                with gr.Row():
         
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                    input_img = gr.inputs.Image(type="pil", label="Original Image")
         
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                    gr.Examples(examples=examples, inputs=[input_img])
         
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         @@ -226,8 +242,8 @@ with gr.Blocks() as app: 
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                    img = gr.outputs.Image(type="pil", label="All Masks")
         
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                    transparent_img = gr.outputs.Image(type="pil", label="Transparent Image")
         
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                with gr.Row():
         
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                all_masks = gr.Gallery(visible=False)
         
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                background = gr.Image(visible=False)
         
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            title = "dacl-challenge @ WACV2024"
         
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            description = """
         
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            <p style="text-align:center">
         
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            <h1>dacl-challenge @ WACV2024</h1>
         
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            </p>
         
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            <b>
         
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            <p style="text-align:center">
         
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            <a href='https://twitter.com/dacl_ai' target='_blank'>Twitter</a><a href='https://x.com/dacl_ai' target='_blank'>/X</a> |
         
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              <li>Model: <a href='https://huggingface.co/nvidia/mit-b1' target='_blank'>SegFormer mit-b1</a>, trained on resized 512x512 images for (only) 10 epochs.</li>
         
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              <li>Label description of dacl10k dataset: "A.3. Class descriptions" in <a href='https://arxiv.org/pdf/2309.00460.pdf' target='_blank'>J. Flotzinger, P.J. Rösch, T. Braml: "dacl10k: Benchmark for Semantic Bridge Damage Segmentation".</a></li>
         
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            </ul> 
         
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            <p></p>
         
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            <p>Workflow: 
         
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             <ul>
         
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              <li>Upload an image or select one from "Examples". </li>
         
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              <li>Then click "1) Generate Masks"</li>
         
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              <li>Select an damage or object type in "Select Label" and choose an "Alpha Factor" for transparancy.</li>
         
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              <li>Then click "2) Generate Transparent Mask (with Alpha Factor)"</li>
         
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            </ul> 
         
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            """
         
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            article = "<p style='text-align: center'><a href='https://github.com/phiyodr/dacl10k-toolkit' target='_blank'>Github Repo</a></p>"
         
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            with gr.Blocks() as app:
         
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                with gr.Row():
         
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                    gr.Markdown(description)
         
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                with gr.Row():
         
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                    input_img = gr.inputs.Image(type="pil", label="Original Image")
         
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                    gr.Examples(examples=examples, inputs=[input_img])
         
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                    img = gr.outputs.Image(type="pil", label="All Masks")
         
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                    transparent_img = gr.outputs.Image(type="pil", label="Transparent Image")
         
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                with gr.Row():
         
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                    dropdown = gr.Dropdown(choices=target_list_all, label="Select Label", value="All")
         
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                    slider = gr.Slider(minimum=0, maximum=1, value=0.4, label="Alpha Factor")
         
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                all_masks = gr.Gallery(visible=False)
         
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                background = gr.Image(visible=False)
         
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