Changzheng Zhang Xiang Xu Dandan Tu*

Abstract
Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection. In this report, we propose a detailed designed Faster RCNN method named FDNet1.0 for face detection. Several techniques were employed including multi-scale training, multi-scale testing, light-designed RCNN, some tricks for inference and a vote-based ensemble method. Our method achieves two 1th places and one 2nd place in three tasks over WIDER FACE validation dataset (easy set, medium set, hard set).
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| face-detection-on-wider-face-easy | FDNet | AP: 0.950 |
| face-detection-on-wider-face-hard | FDNet | AP: 0.896 |
| face-detection-on-wider-face-medium | FDNet | AP: 0.939 |
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