DPT/README.md · kadirnar/ZeST at ba59f3dfcf676ffecaf553733a0792dc29bf112b

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

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

Monodepth:

Segmentation:

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

Place one or more input images in the folder input.
Run a monocular depth estimation model:
```
python run_monodepth.py
```
Or run a semantic segmentation model:
```
python run_segmentation.py
```
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:

Monodepth finetuned on KITTI: dpt_hybrid_kitti-cb926ef4.pt Mirror
Monodepth finetuned on NYUv2: dpt_hybrid_nyu-2ce69ec7.pt Mirror

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