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:

Slides:
https://github.com/MLForNerds/YOLO-OBJECT-DETECTION-TUTORIALS

Neural Networks From Scratch Playlist:

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

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