Скачать с ютуб YOLO-V4: CSPDARKNET, SPP, FPN, PANET, SAM || YOLO OBJECT DETECTION SERIES в хорошем качестве

YOLO-V4: CSPDARKNET, SPP, FPN, PANET, SAM || 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 about Backbone CSPDarknet-53, SPP, FPN, PANT and SAM modules. These are all parts of Bag of Specials in YoloV4. YOLO Playlist:    • YOLO OBJECT DETECTION SERIES || Diggi...   Neural Networks From Scratch Playlist:    • Neural Networks From Scratch in Python   Link to Papers: YoloV4: https://arxiv.org/pdf/2004.10934.pdf DenseNet: https://arxiv.org/pdf/1608.06993.pdf CSPNet: https://arxiv.org/pdf/1911.11929.pdf FPN: https://arxiv.org/pdf/1612.03144.pdf PANET: https://arxiv.org/pdf/1803.01534.pdf Mask-RCNN: https://arxiv.org/pdf/1703.06870.pdf SCRDet: https://arxiv.org/pdf/1811.07126.pdf SAM: https://arxiv.org/pdf/1807.06521.pdf SPP: https://arxiv.org/pdf/1406.4729.pdf Chapters: 00:00 Introduction 00:39 Topics Covered in this video 00:53 YoloV4 Backbone 02:15 Dense Block 03:49 DenseNet Architecture 06:51 CSPNet Intuition 08:00 CSP + DenseNet 10:15 CSP + DarkNet53 & Mish 11:24 Need of Neck Module (Intuition) 18:15 FPN Intuition 21:30 PAN Need & Intuition 27:34 Adaptive Feature Pooling Intuition 35:25 Adaptive Feature Pooling Explanation 38:24 Modified PAN in YoloV4 39:40 Spatial Pyramid Pooling 44:08 Attention Mechanism Intuition 46:40 Spatial Attention Module (SAM) 49:26 Modified SAM in YoloV4 50:42 Conclusion #yolo #yoloobjectdetection #objectdetection #yolov4 #yolov5 #yolov3 #yolov7 #computervision #imageclassification