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Update README.md

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  <img width="586" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/124376591-b312b780-dce2-11eb-80ad-9129d6f5eedb.png">
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- the Pytorch, JAX/Flax based code imlementation of this paper : https://arxiv.org/abs/2104.00677
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  The model gives the 3D neural scene representation (NeRF: Neural Radiances Field) estimated from a few images.
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  Which is based on extracting the semantic information using a pre-trained visual encoder such as CLIP, a Vision Transformer
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  ## Installation
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- ```
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  # Clone the repo
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  svn export https://github.com/google-research/google-research/trunk/jaxnerf
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  # Create a conda environment, note you can use python 3.6-3.8 as
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  --train_dir=/PATH/TO/THE/PLACE/YOU/WANT/TO/SAVE/CHECKPOINTS \
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  --config=configs/CONFIG_YOU_LIKE
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  ```
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- you can toggle the semantic loss by “use_semantic_loss” in cofiguration files.
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  ## Rendered examples by 8-shot learned Diet-NeRF
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  - Lego
 
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  <img width="586" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/124376591-b312b780-dce2-11eb-80ad-9129d6f5eedb.png">
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+ the Pytorch, JAX/Flax based code implementation of this paper : https://arxiv.org/abs/2104.00677
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  The model gives the 3D neural scene representation (NeRF: Neural Radiances Field) estimated from a few images.
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  Which is based on extracting the semantic information using a pre-trained visual encoder such as CLIP, a Vision Transformer
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  ## Installation
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+ ```bash
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  # Clone the repo
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  svn export https://github.com/google-research/google-research/trunk/jaxnerf
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  # Create a conda environment, note you can use python 3.6-3.8 as
 
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  --train_dir=/PATH/TO/THE/PLACE/YOU/WANT/TO/SAVE/CHECKPOINTS \
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  --config=configs/CONFIG_YOU_LIKE
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  ```
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+ You can toggle the semantic loss by “use_semantic_loss” in configuration files.
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  ## Rendered examples by 8-shot learned Diet-NeRF
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  - Lego