In this competition, you’ll benchmark machine learning models on a challenging large-scale dataset. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features. You also have the opportunity to create new features to improve your results.
In this track of the Challenge, you are asked to detect pairs of objects and the relationships that connect them.
The training set contains 329 relationship triplets with 375k training samples. These include both human-object relationships (e.g. "woman playing guitar", "man holding microphone"), object-object relationships (e.g. "beer on table", "dog inside car"), and also considers object-attribute relationships (e.g."handbag is made of leather" and "bench is wooden").
In this challenge, the task is to detect truly suspicious events and false alarms within the set of so-called network traffic alerts, that the Security Operations Center (SOC) Team members @ SOD have to analyze on an everyday basis. An efficient classification model could help the SOC Team to optimize their operations significantly. It is worth adding that although the competition sponsor is entirely commercial, the knowledge and experience that can be gathered by the competition participants may be highly beneficial to improve the intelligent cybersecurity modules in many organizations.
In this track of the Challenge, you are asked to predict a tight bounding box around object instances.
The training set contains 12.2M bounding-boxes across 500 categories on 1.7M images. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (7 per image on average).