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:

• dpt_hybrid-midas-501f0c75.pt, Mirror
• dpt_large-midas-2f21e586.pt, Mirror

Segmentation:

• dpt_hybrid-ade20k-53898607.pt, Mirror
• dpt_large-ade20k-b12dca68.pt, Mirror

2. 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:

• 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