All benchmarks, analysis and links to official package implementations can be found in this repository
Mish also was recently used for a submission on the Stanford DAWN Cifar-10 Training Time Benchmark where it obtained 94% accuracy in just 10.7 seconds which is the current best score on 4 GPU and second fastest overall. Additionally, Mish has shown to improve convergence rate by requiring less epochs. Reference -
Mish also has shown consistent improved ImageNet scores and is more robust. Reference -
Additional ImageNet benchmarks along with Network architectures and weights are avilable on my repository.
Summary of Vision related results:
It would be nice to have Mish as an option within the activation function group.
This is the comparison of Mish with other conventional activation functions in a SEResNet-50 for CIFAR-10:
Mish is a novel activation function proposed in this paper. It has shown promising results so far and has been adopted in several packages including:
All benchmarks, analysis and links to official package implementations can be found in this repository
Mish also was recently used for a submission on the Stanford DAWN Cifar-10 Training Time Benchmark where it obtained 94% accuracy in just 10.7 seconds which is the current best score on 4 GPU and second fastest overall. Additionally, Mish has shown to improve convergence rate by requiring less epochs. Reference -
Mish also has shown consistent improved ImageNet scores and is more robust. Reference -
Additional ImageNet benchmarks along with Network architectures and weights are avilable on my repository.
Summary of Vision related results:
It would be nice to have Mish as an option within the activation function group.
This is the comparison of Mish with other conventional activation functions in a SEResNet-50 for CIFAR-10: