  We propose $\textit{Mish}$, a novel self-regularized non-monotonic activation
function which can be mathematically defined as: $f(x)=x\tanh(softplus(x))$. As
activation functions play a crucial role in the performance and training
dynamics in neural networks, we validated experimentally on several well-known
benchmarks against the best combinations of architectures and activation
functions. We also observe that data augmentation techniques have a favorable
effect on benchmarks like ImageNet-1k and MS-COCO across multiple
architectures. For example, Mish outperformed Leaky ReLU on YOLOv4 with a
CSP-DarkNet-53 backbone on average precision ($AP_{50}^{val}$) by 2.1$\%$ in
MS-COCO object detection and ReLU on ResNet-50 on ImageNet-1k in Top-1 accuracy
by $\approx$1$\%$ while keeping all other network parameters and
hyperparameters constant. Furthermore, we explore the mathematical formulation
of Mish in relation with the Swish family of functions and propose an intuitive
understanding on how the first derivative behavior may be acting as a
regularizer helping the optimization of deep neural networks. Code is publicly
available at https://github.com/digantamisra98/Mish.
