import streamlit as st
from streamlit_extras.switch_page_button import switch_page

st.title("Depth Anything V2")

st.success("""[Original tweet](https://twitter.com/mervenoyann/status/1803063120354492658) (June 18, 2024)""", icon="ℹ️")
st.markdown(""" """)

st.markdown("""
I love Depth Anything V2 😍  
It’s <a href='Depth_Anything' target='_self'>Depth Anything</a>, but scaled with both larger teacher model and a gigantic dataset! Let’s unpack 🤓🧶!
""", unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/Depth_Anything_v2/image_1.jpg", use_column_width=True)
st.markdown(""" """)

st.markdown("""
The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:  
🔖 Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc)  
🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage
""")
st.markdown(""" """)

st.image("pages/Depth_Anything_v2/image_2.jpg", use_column_width=True)
st.markdown(""" """)

st.markdown("""
The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐  
But they still fail encountering real images that have wide distribution in labels 🥲
""")
st.markdown(""" """)

st.image("pages/Depth_Anything_v2/image_3.jpg", use_column_width=True)
st.markdown(""" """)

st.markdown("""
Depth Anything v2 framework is to...  
🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images   
🏷️ Label 62M real images using teacher model  
🦕 Train a student model using the real images labelled by teacher  
Result: 10x faster and more accurate than Marigold!  
""")
st.markdown(""" """)

st.image("pages/Depth_Anything_v2/image_4.jpg", use_column_width=True)
st.markdown(""" """)

st.markdown("""
The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!  
I have created a [collection](https://t.co/3fAB9b2sxi) that has the models, the dataset, the demo and CoreML converted model 😚
""")
st.markdown(""" """)

st.info("""
Ressources:    
[Depth Anything V2](https://arxiv.org/abs/2406.09414)  
by Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao (2024)  
[GitHub](https://github.com/DepthAnything/Depth-Anything-V2)  
[Hugging Face documentation](https://huggingface.co/docs/transformers/model_doc/depth_anything_v2)""", icon="📚")

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st.markdown(""" """)
col1, col2, col3 = st.columns(3)
with col1:
    if st.button('Previous paper', use_container_width=True):
        switch_page("DenseConnector")
with col2:
    if st.button('Home', use_container_width=True):
        switch_page("Home")
with col3:
    if st.button('Next paper', use_container_width=True):
        switch_page("Florence-2")