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# Evaluation with LGT-Net
This is instruction for evaluating our dataset with "[LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network](https://arxiv.org/abs/2203.01824)".
# Downloading Pre-trained Weights
Pre-trained weights are provided by authors on individual datasets at [here](https://drive.google.com/drive/folders/1bOZyXeuNnwFEC9nw7EgJUwMiI685obdT?usp=sharing).
These models are used in our dataset paper below.
- [mp3d/best.pkl](https://drive.google.com/file/d/1o97oAmd-yEP5bQrM0eAWFPLq27FjUDbh/view?usp=sharing): Training on MatterportLayout dataset
- [pano/best.pkl](https://drive.google.com/file/d/1JoeqcPbm_XBPOi6O9GjjWi3_rtyPZS8m/view?usp=sharing): Training on PanoContext(train)+Stanford2D-3D(whole) dataset
- [s2d3d/best.pkl](https://drive.google.com/file/d/1PfJzcxzUsbwwMal7yTkBClIFgn8IdEzI/view?usp=sharing): Training on Stanford2D-3D(train)+PanoContext(whole) dataset
Make sure the pre-trained weight files are stored as follows:
```
checkpoints
|-- SWG_Transformer_LGT_Net
| |-- mp3d
| | |-- best.pkl
| |-- pano
| | |-- best.pkl
| |-- s2d3d
| | |-- best.pkl
```
# Preparing Dataset
You can use `assets/layout_eval/convert4LGTNet.py` to get proper data structure for evaluation.
### Evaluation with MatterportLayout
Make sure the dataset files are stored as follows:
```
src/dataset/mp3d
|-- image
| |-- 000_<scene>_equi_rgb.png
|-- label
| |-- 000_<scene>_equi_layout.json
|-- split
|-- test.txt # it needs to contain all of files.
```
### Evaluation with PanoContext
Make sure the dataset files are stored as follows:
```
src/dataset/pano_s2d3d
|-- test
| |-- img
| | |-- pano_000_<scene>_equi_rgb.png
| |-- label_cor
| |-- pano_000_<scene>_equi_layout.txt
```
### Evaluation with Stanford 2D-3D
Make sure the dataset files are stored as follows:
```
src/dataset/pano_s2d3d
|-- test
| |-- img
| | |-- camera_000_<scene>_equi_rgb.png
| |-- label_cor
| |-- camera_000_<scene>_equi_layout.txt
``` |