Vision Transformers for Dense Prediction

This repository contains code and models for:

Vision Transformers for Dense Prediction

Setup

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

Monodepth:

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

Segmentation:

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

2. Set up dependencies:

   conda install pytorch torchvision opencv 
   pip install timm

   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_segmentation, respectively.

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