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A real time face detection
(2021)
Deep convolutional neural networks have been achieving the state-of-art results on the many computer vision tasks (i.e., object detection, object recognition, instance segmentation, semantic segmentation and many more). However, deep-CNNs are very difficult to utilize for real time applications (Autonomous driving, Driver occupant monitoring systems etc.) and hard to deploy on small devices (Mobile, Embedded system, FPGAs and many more), due to their size. Therefore, in this project, modern object detector (YOLOv4-tiny) is trained on wider-face dataset with BOF(Bag of Freebies) and BOS(Bag of Special) strategies to detect the faces robustly and final network is also compressed to deploy on the portable devices with limited resources. Proposed face detector achieves 90.04% mAP, 80.88% mAP, 73.20% mAP on easy, medium and hard categories of the customized wider face dataset, respectively with occupying only 9,5MB storage.