YOLO-V4: MiWRC, CmBN, DROP BLOCK, CIOU, DIOU || YOLO OBJECT DETECTION SERIES скачать в хорошем качестве

YOLO-V4: MiWRC, CmBN, DROP BLOCK, CIOU, DIOU || YOLO OBJECT DETECTION SERIES

This video is about Yolo object detection family. This is about YoloV4 which is the most popular and widely used object detector in the industry. YoloV4 has the highest usage by industry for commercial purposes because of its optimal speed and accuracy. In this video, we discussed Multi-Input Weighted Residual Connections, Cross mini Batch Normalization, Drop Block Regularisation, types of IOU losses. These are all parts of Bag of Specials and Bag of Features in YoloV4. YOLO Playlist:    • YOLO OBJECT DETECTION SERIES || Digging in...   Slides: https://github.com/MLForNerds/YOLO-OB... Neural Networks From Scratch Playlist:    • Neural Networks From Scratch in Python   Link to Papers: YoloV4: https://arxiv.org/pdf/2004.10934.pdf EfficientDet: https://arxiv.org/pdf/1911.09070.pdf Cross Batch Norm: https://arxiv.org/pdf/2002.05712.pdf DropBlock Regularization: https://arxiv.org/pdf/1810.12890.pdf IOU Losses: https://arxiv.org/pdf/1911.08287.pdf Chapters: 00:00 Introduction 02:00 Cross Mini-Batch Normalization 11:06 Multi-Input Weighted Residual Connections 17:50 Drop Block Regularization 25:57 IOU Loss 30:32 GIOU Loss 34:29 DIOU Loss 37:29 CIOU Loss 43:06 Conclusion #yolo #yoloobjectdetection #objectdetection #yolov4 #yolov5 #yolov3 #yolov7 #computervision #imageclassification