CMU-Net advanced version can be found inCMUNeXt🥰 New benchmarks are available for fair comparison 😎:url😘 Introduction U-Net and its extended segmentation model have achieved great success in medical image segmentation tasks. However, due to the inherent local characteristics of ordinary conv...
This study offers a CMU-Net MFM SR technique based on this challenge, which combines a modified U-Net plus a Max-Feature-Map (MFM) module and a mix of BCE loss, Cosine matrix loss, and magnitude loss to restore the identity result of the SR image. With a classification accuracy of ...
Solved: Hi There, We've got 2 problems for the moment and some lack of information to solve it. The specs: CMU 7.2 - ProLiant SL4540 Gen8 + RHES 6.4 - Case:
用基本的训练参数配置就可以把 ResNet-50 在 ImageNet-1K 从头开始 (from scratch) 训练到 80.1% (不使用 mixup,cutmix 等数据增强),训练速度(尤其是数据读取开销)相比传统分类框架节省 16% 以上,比之前 SOTA 算法快 30% 以上,是目前精度和速度双双最优的知识蒸馏策略之一,代码和模型已全部开源!
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该文是CMU的Zhiqiang Shen提出的一种提升标准ResNet50精度的方法,它应该是首个将ResNet50的Top1精度刷到80%+的(无需额外数据,无需其他tricks,无需网络架构调整)。该文对于研究知识蒸馏的同学应该是有不少可参考的价值,尤其是里面提到的几点讨论与结论,值得深思。
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而从网络内部的Activation分布中也可以看出,增加了RSign 和RPReLU的ReActNet有更强的表达能力,可以捕捉到更精细的Activation Distribution。2 结论 在本文中,作者提出了一些新的想法来优化1比特CNN以获得更高的精度。首先,设计了基于MobileNetV1的无参数快捷方式,在普通卷积层和下采样层中传播实值特征映射。在Image...
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Player is the centralized interface where users, teams, and administrators go to configure and participate in the cyber exercise. - .NET 8 update (#44) · cmu-sei/Player.Api@2d71d9d