The AI revolution continues, and there is no indication of it nearing the finish line. The last year has brought astonishing developments in two critical areas of generative modeling: large language models and diffusion models.
With machine learning-powered tools, brands can evaluate a campaign’s ROI by analyzing video coverage of sponsored events. Thanks to computer vision solutions, it is possible to precisely determine the time and place a brand was positioned during a sporting event.
In this post we present how to approach product recognition through the example of our solution for the iMaterialist challenge announced by CVPR and Google.
In the previous post we explained what region of interest pooling (RoI pooling for short) is. In this one, we present an example of applying RoI pooling in TensorFlow. We base it on our custom RoI pooling TensorFlow operation. We also use Neptune as a support in our experiment performance tracking.
