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

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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>

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

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