Popularity of mobile vision applications constantly increases. These Artificial Intelligence technology-driven solutions exhibit high potential for the dynamically-changing landscape of education, in which they could be used as powerful tools to engage and motivate learners. The integration of game-like elements into educational software can be a very effective approach to capture the attention of students by providing interactive and immersive learning experiences. That is why in this study we present a category recognition method for computationally-limited devices such as smartphones to be used in a cost-efficient manner in the areas connected to interactive education. We use small and efficient ImageNet-trained Convolutional Neural Networks and Deep Template Matching to create a classification solution. To test our solution, we construct a specialized image dataset with different toys and items that could be suitable for early-stage education to engage young learners. We perform an extensive study on how the number of images used to create templates and different similarity/distance measures applied for matching impact the accuracy to provide practical observations that can be used to create real-life game-like elements for educational software. Our results indicate that utilizing more templates and similarity measures results in better generalization, representativeness, and robustness, particularly when it comes to rotation, which is crucial for mobile applications. We release our data and code via our GitHub repository at https://github.com/iitis/EduToyz.

Classification is the key task of deep learning. Among others, it is used in computer vision for object recognition, and in natural language processing for the masked language modeling. All of these tasks are crucial for modern automatic and semi-automatic production facilities powered by Automated Guided Vehicles (AGVs), because they enable intelligent inspection, maintenance log analysis and operation control. Especially in such safety-critical domains, in which humans and machines often coexist, it is crucial to provide new methods that could help us understand the operation of these black-boxes. That is why we present a novel visual-analytic methods that can be used to examine how networks perceive relations/similarities between the known classes. Our methods operate solely on the trained models and do not require any data samples. They can also reveal some quality issues in the training datasets and indicate low model accuracy. Our methods are suitable for generic vision and language models, but can also be used in transfer learning scenarios. To empirically validate our approach in such a scenario, we conduct experiments on a state-of-the-art mobile vision model - MobileNetV2 - fine-tuned for vehicle classification. We release a new dataset - UtilityVehicles - featuring images of various vehicles that can occur in industry. The presented use case is a vision-based application for Augmented Reality applications for smartphones and embedded devices for AGVs in automatic and semi-automatic production facilities. We release a GitHub repository with data and code: https://github.com/iitis/UtilityVehicles.