https://github.com/ashutosh1807/pixelformer

https://github.com/ashutosh1807/pixelformer

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• Host: GitHub
• URL: https://github.com/ashutosh1807/pixelformer
• Owner: ashutosh1807
• License: mit
• Created: 2022-10-13T10:30:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-02-13T06:28:42.000Z (over 1 year ago)
• Last Synced: 2023-03-06T18:56:45.178Z (over 1 year ago)
• Language: Python
• Size: 1010 KB
• Stars: 53
• Watchers: 13
• Forks: 4
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-Monocular-Depth - PixelFormer: Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention

README

        
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-attention-everywhere-monocular/monocular-depth-estimation-on-nyu-depth-v2)](https://paperswithcode.com/sota/monocular-depth-estimation-on-nyu-depth-v2?p=attention-attention-everywhere-monocular)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-attention-everywhere-monocular/monocular-depth-estimation-on-kitti-eigen)](https://paperswithcode.com/sota/monocular-depth-estimation-on-kitti-eigen?p=attention-attention-everywhere-monocular)

## PixelFormer: Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention

This is the official PyTorch implementation for WACV 2023 paper 'Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention'.

**[Paper](https://arxiv.org/pdf/2210.09071)** 


### Installation

```

conda create -n pixelformer python=3.8

conda activate pixelformer

conda install pytorch=1.10.0 torchvision cudatoolkit=11.1

pip install matplotlib tqdm tensorboardX timm mmcv

```

### Datasets

You can prepare the datasets KITTI and NYUv2 according to [here](https://github.com/cleinc/bts), and then modify the data path in the config files to your dataset locations.

### Training

First download the pretrained encoder backbone from [here](https://github.com/microsoft/Swin-Transformer), and then modify the pretrain path in the config files.

Training the NYUv2 model:

```

python pixelformer/train.py configs/arguments_train_nyu.txt

```

Training the KITTI model:

```

python pixelformer/train.py configs/arguments_train_kittieigen.txt

```

### Evaluation

Evaluate the NYUv2 model:

```

python pixelformer/eval.py configs/arguments_eval_nyu.txt

```

Evaluate the KITTI model:

```

python pixelformer/eval.py configs/arguments_eval_kittieigen.txt

```

## Pretrained Models

* You can download the pretrained models "nyu.pt" and "kitti.pt" from [here](https://drive.google.com/drive/folders/1Feo67jEbccqa-HojTHG7ljTXOW2yuX-X?usp=share_link).

## Citation

If you find our work useful in your research, please cite the following:

```bibtex

@InProceedings{Agarwal_2023_WACV,

    author    = {Agarwal, Ashutosh and Arora, Chetan},

    title     = {Attention Attention Everywhere: Monocular Depth Prediction With Skip Attention},

    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

    month     = {January},

    year      = {2023},

    pages     = {5861-5870}

}

```

## Contact

For questions about our paper or code, please contact ([@ashutosh1807](https://github.com/ashutosh1807)) or raise an issue on GitHub.

### Acknowledgements

Most of the code has been adpated from CVPR 2022 paper [NewCRFS](https://github.com/aliyun/NeWCRFs). We thank Weihao Yuan for releasing the source code for the same.

Also, thanks to Microsoft Research Asia for opening source of the excellent work [Swin Transformer](https://github.com/microsoft/Swin-Transformer).