Electronic Devices Dataset for 2-Class Semantic Segmentation

<p><span>This dataset contains images used in the monograph titled&nbsp;<em>Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej</em>&nbsp;(Application of Selected Deep Learning Methods in Computer Vision) to build the U-Net model. The full collection consists of 600 image files of resolution 512x512 pixels showing small electronic devices and office accessories (<a title="Electronic Devices Dataset" href="https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link</a>). The set was randomly divided into a training part (50% of the full set), validation and test part (each accounted for 25% of the full set). As a result, the training part contains 300 files, validation part &ndash; 150 and test part - 150. The collection was created by augmenting the original set of 100 images with vertical and horizontal flip, random rotation from -45 to 45 degrees, and a combination of both flips and random rotation. The images are labeled with masks representing 2 kind of objects &ndash; REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.</span></p>