An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

22 Apr 2019Youngwan LeeJoong-won HwangSangrok LeeYuseok BaeJongyoul Park

As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task. Although feature reuse enables DenseNet to produce strong features with a small number of model parameters and FLOPs, the detector with DenseNet backbone shows rather slow speed and low energy efficiency... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Instance Segmentation COCO test-dev VoVNetV1-57 mask AP 40.8% # 9
Instance Segmentation COCO test-dev VoVNetV1-39 mask AP 39.7% # 11