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pages/8_Depth_Anything.py
CHANGED
@@ -25,7 +25,7 @@ What the authors have done:
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""")
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st.markdown(""" """)
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st.image("pages/
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st.markdown(""" """)
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st.markdown("""
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@@ -36,7 +36,7 @@ The architecture consists of <a href='DINOv2' target='_self'>DINOv2</a> encoder
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st.markdown(""" """)
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st.image("pages/
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st.markdown(""" """)
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st.markdown("""Thanks to this, Depth Anything performs very well! I have also benchmarked the inference duration of the model against different models here. I also ran `torch.compile` benchmarks across them and got nice speed-ups π
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@@ -64,7 +64,7 @@ On T4 GPU, mean of 30 inferences for each. Inferred using `pipeline` (pre-proces
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""")
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st.markdown(""" """)
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st.image("pages/
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st.markdown(""" """)
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st.markdown("""
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@@ -74,7 +74,7 @@ See all the available Depth Anything checkpoints [here](https://t.co/Ex0IIyx7XC)
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""")
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st.markdown(""" """)
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st.image("pages/
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st.markdown(""" """)
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st.info("""
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""")
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st.markdown(""" """)
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st.image("pages/Depth Anything/image_1.jpg", use_column_width=True)
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st.markdown(""" """)
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st.markdown("""
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st.markdown(""" """)
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st.image("pages/Depth Anything/image_1.jpg", use_column_width=True)
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st.markdown(""" """)
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st.markdown("""Thanks to this, Depth Anything performs very well! I have also benchmarked the inference duration of the model against different models here. I also ran `torch.compile` benchmarks across them and got nice speed-ups π
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""")
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st.markdown(""" """)
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st.image("pages/Depth Anything/image_2.jpg", use_column_width=True)
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st.markdown(""" """)
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st.markdown("""
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""")
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st.markdown(""" """)
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st.image("pages/Depth Anything/image_3.jpg", use_column_width=True)
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st.markdown(""" """)
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st.info("""
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