The VSP Dataset

Dataset Structure

The folder structure of the VSP dataset is as follows:

{root}/{split}/{date}/{profile}/{defect}/{layer}___{board}_{number}{type}{ext}

The meaning of the individual elements is:

• root the root folder of the Cityscapes dataset. Many of our scripts check if an environment variable VSP_DATASET pointing to this folder exists and use this as the default choice.
• split the split, i.e. train/val/test/train_extra/demoVideo. Note that not all kinds of data exist for all splits. Thus, do not be surprised to occasionally find empty folders.
• data time of data collection. eg. AIVS_20xx_xx_xx.
• profile ID of images generated by computer Aided Manufacturing.
• defect positive and negative defects.
• layer layer number of multi-layer PCB.
• board ID of PCB images.
• number number of defects.
• ext the extension of the file and optionally a suffix, e.g. _labelTrainIds.png for ground truth files

More types might be added over time and also not all types are initially available. Please let us know if you need any other meta-data to run your approach.

Possible values of split

• train usually used for training, contains 2975 images with fine and coarse annotations
• val should be used for validation of hyper-parameters, contains 500 image with fine and coarse annotations. Can also be used for training.

Scripts

Installation

Install vspscripts with setup.py

python setup.py build
python setup.py sdist
pip install . # or "python setup.py install" 

Install vspscripts in develop

python setup.py build
python setup.py develop 

# uninstall in develop 
python setup.py develop --uninstall

Install vspscripts with pip

python -m pip install vspscripts

Graphical tools (viewer and label tool) are based on Qt5 and can be installed via

python -m pip install vspscripts[gui]

Usage

The installation installs the vspscripts scripts as a python module named vspscripts and exposes the following tools

Package Content

The package is structured as follows

• helpers: helper files that are included by other scripts
• viewer: view the images and the annotations
• preparation: convert the ground truth annotations into a format suitable for your approach
• evaluation: validate your approach
• annotation: the annotation tool used for labeling the dataset

Note that all files have a small documentation at the top. Most important files

• helpers/labels.py: central file defining the IDs of all semantic classes and providing mapping between various class properties.