# Knowledge Distillation for Object Detection: from generic to remote sensing datasets Source: [Knowledge Distillation for Object Detection: from generic to remote sensing datasets]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:24:25.429Z Authors: Ho\\\`ang-\\^An L\\^e, Minh-Tan Pham Published: 2023-07-18T13:49:00.000Z ## Abstract Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a remote sensing context various off-the-shelf object detection knowledge distillation methods which have been originally developed on generic computer vision datasets such as Pascal VOC. In particular, methods covering both logit mimicking and feature imitation approaches are applied for vehicle detection using the well-known benchmarks such as xView and VEDAI datasets. Extensive experiments are performed to compare the relative performance and interrelationships of the methods. Experimental results show high variations and confirm the importance of result aggregation and cross validation on remote sensing datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()