cnn2 SENet(Squeeze-and-Excitation Networks) SENet Squeeze-and-Excitation Networks The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broa arxiv.org 어떤 문제를 맞닥뜨렸을때, 그리고 그 문제를 머신러닝으로 해결하려고 하면 아직까지도 막연하게 '잘 되지 않을까?'라는 생각이 조금은 남아있는 것 같습니다. 하지만 실제.. 2022. 1. 5. SSD: Single Shot MultiBox Detector SSD: Single Shot MultiBox Detector SSD is an object detection algorithm after the Yolo-v1, improved the mAP and speed. Yolo achieved a dramatic performance improvement in terms of speed compared to the two-step algorithm of the RCNN sereis, but there was a limit in accuracy. input : 300x300 3 channel image output : bounding box, object class Backbone network : VGG-16 Multiscale Feature Maps As y.. 2021. 6. 27. 이전 1 다음
