rwightman/pytorch-image-models: MaxxVit (CoAtNet, MaxVit, and related experimental weights)

CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) timm trained weights Weights were created reproducing the paper architectures and exploring timm sepcific additions such as ConvNeXt blocks, parallel partitioning, and other experiments. Weights were trained on a mix of TPU and GPU systems. Bulk of weights were trained on TPU via the TRC program (https://sites.research.google/trc/about/). CoAtNet variants run particularly well on TPU, it's a great combination. MaxVit is better suited to GPU due to the window partitioning, although there are some optimizations that can be made to improve TPU padding/utilization incl using 256x256 image size (8, 8) windo/grid size, and keeping format in NCHW for partition attention when using PyTorch XLA