Mish
Mish is a self-regularized non-monotonic neural activation function designed to improve the performance of deep neural networks. Introduced in the paper "Mish: A Self Regularized Non-Monotonic Neural Activation Function" presented at BMVC 2020, this activation function combines the benefits of smoothness and non-monotonicity to facilitate better gradient flow and learning dynamics. It serves as a powerful alternative to traditional activation functions like ReLU and Swish, often yielding state-of-the-art results in image classification and object detection tasks. The official repository provides implementations in PyTorch, complete with pretrained models, benchmarking scripts, and compatibility with various frameworks including Google AutoML, Sony Nnabla, and OpenVINO. The project includes resources for understanding loss landscapes and offers optimized CUDA versions for faster inference. Widely cited in academic research, Mish is extensively utilized by the machine learning community for enhancing model accu