As classifying images into categories is a ubiquitous task occurring in various domains, a need for a machine learning pipeline which can accommodate for new categories is easy to justify. In particular, common general requirements are to filter out low-quality (blurred, low contrast etc.) images, and to speed up the learning of new categories if image quality is sufficient. In this blog post, resulting from a joint work with Aigiz Kunafin (Trustbit Data Science), we compare several image classification models from the transfer learning perspective.

In the previous blog post, we created an already pretty powerful image segmentation model in order to detect the shape of truck parking lots on satellite images. However, we will now try to run the code on new hardware and get even better as well as more robust results.

Real-time truck tracking is crucial in logistics: to enable accurate planning and provide reliable estimation of delivery times, operators build detailed profiles of loading stations, providing expected durations of truck loading and unloading, as well as resting times. Yet, how to derive an exact truck status based on mere GPS signals?