This dataset contains images used in the monograph titled Computer Vision in Python. Practical Applications of Deep Learning (Computer vision w Pythonie. Praktyczne zastosowania uczenia głębokiego) to build the Faster R-CNN model. The full collection consists of 600 image files showing 6 classes of objects: kot (cat), krowa (cow), pies (dog), koń (horse), człowiek (human), owca (sheep). All images were scaled so that the smaller side is no shorter than 600 pixels and the larger side is no longer than 1000 pixels. The set was randomly divided into a training part (75% of the full set) and a test part (25% of the full set). As a result, the training part contains 450 files, and the test part - 150. Both parts are balanced - they contain a similar number of detected objects. The images are labeled with bounding boxes in the Pacal VOC format, according to which the description of each file is contained in an XML file with the same name. The dataset can be used to build models for object detection.