Skip to content

ojalar/wcamnet

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

WCamNet

Implementation of WCamNet from the paper "Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features"

Installation

Run:

pip3 install -r requirements

Usage

To train and validate, run:

python3 train_wcamnet.py -tr <path-to-train-csv> -v <path-to-val-csv> -lr <learning-rate> -wd <weight-decay> -s <path-to-save-directory> -n <name-of-run>

To test

python3 test_wcamnet.py -w <path-to-weight-file> -te <path-to-test-csv> -s <path-to-save-directory> -n <name-of-run>

.csv data format

The training/validation/testing data should be provided as a .csv-files, which are formatted as

<path-to-image>, <friction-value>

About

Implementation of WCamNet from the paper "Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages