The SpectroVision dataset and associated code for the paper "Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging"

git clone https://github.com/Healthcare-Robotics/spectrovision.git
cd spectrovision
# Setup a virtual python environment to install requirements
python3 -m pip install --user virtualenv
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txt

Download the processed image and spectral data used for training neural network models:

wget -O dataset/spectrovision_dataset.zip https://github.com/Healthcare-Robotics/spectrovision/releases/download/v1.0/spectrovision_dataset.zip
unzip dataset/spectrovision_dataset.zip -d dataset

Download the raw images and spectral measurements:

wget -O dataset/spectrovision_raw_dataset.zip https://www.dropbox.com/s/mrqmx437m7gykjg/spectrovision_raw_dataset.zip?dl=1
unzip dataset/spectrovision_raw_dataset.zip -d dataset

Note: These computations will take a long time. Computing leave-one-object-out results for a training set of 104 material objects requires training 104 neural networks.

python3 main.py

Results are currently averaged over 10 seeds (which takes 10 times as long). If you wish to use only a single seed to speed up result computations (at the risk of large variation in results), you can use the following command:

python3 main.py --seed 8000

You may also run the script in the background using nohup. This is especially helpful when computing results on a remote machine.

nohup python3 main.py > results.out &