48 lines
1.5 KiB
Markdown
48 lines
1.5 KiB
Markdown
# Finetuning Airbert on Downstream VLN Tasks
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This repository stores the codebase for finetuning [Airbert](https://github.com/airbert-vln/airbert) on downstream VLN tasks including R2R and REVERIE. The code is based on [Recurrent-VLN-BERT](https://github.com/YicongHong/Recurrent-VLN-BERT). We acknowledge [Yicong Hong](https://github.com/YicongHong) for releasing the Recurrent-VLN-BERT code.
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## Prerequisites
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1. Follow instructions in [Recurrent-VLN-BERT](https://github.com/YicongHong/Recurrent-VLN-BERT#prerequisites) to setup the environment and download data.
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2. Download the [trained models]().
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## REVERIE
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### Inference
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To replicate the performance reported in our paper, load the trained models and run validation:
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```bash
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bash scripts/valid_reverie_agent.sh 0
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```
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### Training
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To train the model, simply run:
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```bash
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bash scripts/train_reverie_agent.sh 0
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```
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## R2R
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### Inference
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To replicate the performance reported in our paper, load the trained models and run validation:
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```bash
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bash scripts/valid_r2r_agent.sh 0
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```
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### Training
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To train the model, simply run:
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```bash
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bash scripts/train_r2r_agent.sh 0
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```
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## Citation
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Please cite our paper if you find this repository useful:
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```
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@misc{guhur2021airbert,
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title ={{Airbert: In-domain Pretraining for Vision-and-Language Navigation}},
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author={Pierre-Louis Guhur and Makarand Tapaswi and Shizhe Chen and Ivan Laptev and Cordelia Schmid},
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year={2021},
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
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}
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```
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