  Object detection is a computer vision field that has applications in several
contexts ranging from biomedicine and agriculture to security. In the last
years, several deep learning techniques have greatly improved object detection
models. Among those techniques, we can highlight the YOLO approach, that allows
the construction of accurate models that can be employed in real-time
applications. However, as most deep learning techniques, YOLO has a steep
learning curve and creating models using this approach might be challenging for
non-expert users. In this work, we tackle this problem by constructing a suite
of Jupyter notebooks that democratizes the construction of object detection
models using YOLO. The suitability of our approach has been proven with a
dataset of stomata images where we have achieved a mAP of 90.91%.
