Model Compression in Object Detection

Compressed neural-network models have a growing relevance in the Deep Learning literature, since they allow the deployment of AI on devices with computational constraints for many automation purposes. Despite the amount and diversity of work for this purpose, there is no standard benchmark in the literature, and it is often difficult to choose the proper approach due to the large difference between models, datasets, and training details that are tested. Therefore, this paper proposes a standard experimental benchmark for different model compression approaches for the object detection task, using a fixed model (the well-known YOLOv3) and training scheme. Between Pruning, Knowledge Distillation, and Neural Architecture Search, our experiments reveal that the best trade-off is by using pruning, which enables the creation of a model with 80.67% mAP of the original model but removing 98.8% of the parameters, 96.53% of the Multiply-Accumulate Operations, and reducing the storage size from 235.44MB to 11.61MB.