{"url":"/method/cspdarknet53","slug":"cspdarknet53","name":"CSPDarknet53","full_name":"CSPDarknet53","full_name_withheld":false,"description_markdown":"**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. \r\n\r\nThis CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).","description_state":"present","introduced_year":null,"introduced_by":{"title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","first_author":"Alexey Bochkovskiy","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object"},"source":{"url":"https://arxiv.org/abs/2004.10934v1","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/be3a20bb4a87988b30dddb018d74ee677d1434e8/tool/darknet2pytorch.py#L134","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural 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