#Vision

Discover the capabilities of the MetaFormer architecture in vision tasks through PoolFormer, which leverages simple pooling for token mixing to outperform advanced transformers and MLP models. This project emphasizes straightforward design while achieving high accuracy on datasets such as ImageNet-1K. Find comprehensive resources including implementations, training scripts, model evaluations, and downloadable pretrained models, along with visualization tools to explore activation patterns in models like PoolFormer, DeiT, and ResNet. Ideal for those interested in simplifying computer vision models without sacrificing performance.

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.