#MambaOut

Delve into the MambaOut PyTorch models introducing groundbreaking efficiency in vision tasks with models like the Kobe edition. MambaOut excels with 80% ImageNet accuracy using minimal resources, integrating with pytorch-image-models for impressive performance. Understand model architecture, and compare causal and RNN-like attention. Find training insights, validation processes, and explore through Hugging Face Spaces' Gradio demo.

The project offers broad updates across multiple model types, providing fixes and support for modern architectural weights. Enhancements include the introduction of models like SigLIP SO400M ViT and RDNet, along with optimizations in MobileNet versions. Advanced configurations and improved hardware compatibility, including Intel Arc and Ascend NPUs, deliver notable performance improvements, making it a key resource for the deep learning community while aligning with current research trends.