Layout-based-sTDE

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Layout-based-sTDE

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Layout-based Causal Inference for Object Navigation (CVPR 2023)

Layout-based-sTDE 최신버젼 다운로드

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// repository documentation

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# Layout-based Causal Inference for Object Navigation

## Setup
- Clone the repository and move into the top-level directory `cd Layout-based-sTDE`
- Create conda environment. `conda env create -f environment.yml`
- Activate the environment. `conda activate ng`
- We provide pre-trained [model](https://drive.google.com/file/d/1RekmCexyY_1IpI__F8FHFlP5n0Al73EX/view?usp=sharing) of ORG+L-sTDE. Please download and put it in the `./trained_models` folder.
- Our settings of dataset follows previous works, please refer to [HOZ](https://github.com/sx-zhang/HOZ.git) for AI2THOR and [SemExp](https://github.com/devendrachaplot/Object-Goal-Navigation.git) for Gibson.  
## Training and Evaluation
### Train our Layout-based model 
```shell
python main.py \
      --title layoutmodel \
      --model LayoutModel \
      --workers 12 \
      --gpu-ids 0
```
### Evaluate our model with sTDE (our Layout-based sTDE model) 
```shell
python full_eval.py \
        --title layoutmodel \
        --model LayoutModel \
        --results-json layoutmodel_sTDE.json \
        --gpu-ids 0 \
        --TDE_self True \
        --TDE_threshold 0.5 \
        --TDE_mode zero
```

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