starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
论文提出了一种新的神经网络架构 FasterNet,旨在通过提高浮点运算每秒(FLOPS)来实现更快的网络速度,同时不牺牲准确性。通过重新审视流行的卷积操作,发现深度可分离卷积(DWConv)等操作虽然减少了浮点运算(FLOPs),但频繁的内存访问导致了低效的FLOPS。为此,作者提出了一
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
VanillaNet,是一种强调简洁性和优雅设计的新型神经网络架构。VanillaNet 通过避免深度结构、跳过连接和复杂的操作(如自注意力机制),实现了在计算机视觉任务中与深度复杂网络相当的性能,同时具有更高的效率和可部署性。
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4 }: if m is RevCol: args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]] args[2] = [max(round(k * depth), 1) for k in args[2]] m = m(*args) c2 = ...
StarNet and LSCD modules, the accuracy is increased by 0.7%, FLOPs are reduced by 0.3 G, the number of parameters is increased by 0.2 M, the model size is increased by 0.5 MB, and the FPS is increased by 5.9. The experimental results show that the C2f-Star module enhances the ...