Papers › Mish: A Self Regularized Non-Monotonic Activation Function
Mish: A Self Regularized Non-Monotonic Activation Function
Diganta Misra
We propose Mish, a novel self-regularized non-monotonic activation function which can be mathematically defined as: f(x)=xtanh(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 (AP₅₀ᵛᵃˡ) by 2.1% in MS-COCO object detection and ReLU on ResNet-50 on ImageNet-1k in Top-1 accuracy by ≈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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | ResNet 9 + Mish | Percentage correct | 94.05 | #164 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | ResNet v2-20 (Mish activation) | Percentage correct | 92.02 | #186 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | ResNet v2-110 (Mish activation) | Percentage correct | 74.41 | #154 of 211 | Archive leaderboard | report |
| Image Classification | ImageNet | CSPResNeXt-50 + Mish | Top 1 Accuracy | 79.8% | #735 of 1060 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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