YOLO v4 uses:
• Bag of Freebies (BoF) for backbone: CutMix and Mosaic data augmentation, DropBlock regularization,Class label smoothing
• Bag of Specials (BoS) for backbone: Mish activation, Cross-stage partial connections (CSP), Multi input weighted residual
connections (MiWRC)
• Bag of Freebies (BoF) for detector: CIoU-loss,CmBN, DropBlock regularization, Mosaic data augmentation, Self-Adversarial
Training, Eliminate grid sensitivity, Using multiple anchors for a single ground truth, Cosine annealing scheduler [52], Optimal
hyperparameters, Random training shapes
• Bag of Specials (BoS) for detector: Mish activation,SPP-block, SAM-block, PAN path-aggregation block,DIoU-NMS
下面是yolov4原论文中给出的方法,请问您的代码全部实现了这些功能吗?如果没有的话具体哪些还没有实现呢?谢谢!
YOLO v4 uses: • Bag of Freebies (BoF) for backbone: CutMix and Mosaic data augmentation, DropBlock regularization,Class label smoothing • Bag of Specials (BoS) for backbone: Mish activation, Cross-stage partial connections (CSP), Multi input weighted residual connections (MiWRC) • Bag of Freebies (BoF) for detector: CIoU-loss,CmBN, DropBlock regularization, Mosaic data augmentation, Self-Adversarial Training, Eliminate grid sensitivity, Using multiple anchors for a single ground truth, Cosine annealing scheduler [52], Optimal hyperparameters, Random training shapes • Bag of Specials (BoS) for detector: Mish activation,SPP-block, SAM-block, PAN path-aggregation block,DIoU-NMS