Vision Transformers for Dense Prediction
This repository contains code and models for our paper:
Vision Transformers for Dense Prediction
Renรฉ 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
weightsfolder:
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_monodepthandoutput_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