Electronic Devices Dataset for 2-Class Semantic Segmentation

This dataset contains images used in the monograph titled Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej (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. 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 – 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 – REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.本数据集包含用于专著《Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej》（英文标题："Application of Selected Deep Learning Methods in Computer Vision"，即《精选深度学习方法在计算机视觉中的应用》）中构建U-Net模型的图像。完整数据集共包含600张分辨率为512×512像素的图像，内容为小型电子设备与办公配件。该数据集经随机划分：训练集占全集的50%，验证集与测试集各占全集的25%，最终训练集含300张图像，验证集与测试集各含150张图像。本数据集通过对初始100张图像执行水平翻转、垂直翻转、-45°至45°随机旋转，以及翻转与随机旋转的组合操作完成数据增强后构建而成。所有图像均带有标注掩码，用于区分两类目标：遥控器（REMOTES）与电池（BATTERIES）。因此，本数据集可用于构建多分类语义分割模型。