/DPT

Dense Prediction Transformers

Primary LanguagePythonMIT LicenseMIT

Vision Transformers for Dense Prediction

This repository contains code and models for our paper:

Vision Transformers for Dense Prediction
René Ranftl, Alexey Bochkovskiy, Vladlen Koltun

Changelog

  • [March 2021] Initial release of inference code and models

Setup

  1. Download the model weights and place them in the weights folder:

Monodepth:

Segmentation:

  1. Set up dependencies:

    pip install -r requirements.txt

    The code was tested with Python 3.7, PyTorch 1.8.0, OpenCV 4.5.1, and timm 0.4.5

Usage

  1. Place one or more input images in the folder input.

  2. Run a monocular depth estimation model:

    python run_monodepth.py

    Or run a semantic segmentation model:

    python run_segmentation.py

  3. The results are written to the folder output_monodepth and output_semseg, respectively.

Use the flag -t to switch between different models. Possible options are dpt_hybrid (default) and dpt_large.

Additional models:

Run with

python run_monodepth -t [dpt_hybrid_kitti|dpt_hybrid_nyu]

Evaluation

Hints on how to evaluate monodepth models can be found here: https://github.com/intel-isl/DPT/blob/main/EVALUATION.md

Citation

Please cite our papers if you use this code or any of the models.

@article{Ranftl2021,
	author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
	title     = {Vision Transformers for Dense Prediction},
	journal   = {ArXiv preprint},
	year      = {2021},
}

@article{Ranftl2020,
	author    = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
	title     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},
	journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	year      = {2020},
}

Acknowledgements

Our work builds on and uses code from timm and PyTorch-Encoding. We'd like to thank the authors for making these libraries available.

License

MIT License