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YOLOv4: Optimal Speed and Accuracy of Object Detection

by   Alexey Bochkovskiy, et al.
Academia Sinica

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5 for the MS COCO dataset at a realtime speed of  65 FPS on Tesla V100. Source code is at


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Code Repositories


Convolutional Neural Networks

view repo


PyTorch ,ONNX and TensorRT implementation of YOLOv4

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YOLO reproduce summary (now based on YOLOv3)

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reproduce the YOLO series of papers in pytorch, including YOLOv4, PP-YOLO, YOLOv5,YOLOv3, etc.

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Object detection of Forza Horizon 4 gameplay video

view repo