maxvit_nano_rw_256.sw_in1k EU AI Act Compliance Profile

Model card for maxvit_nano_rw_256.sw_in1k

A timm specific MaxViT image classification model. Trained in timm on ImageNet-1k by Ross Wightman.

ImageNet-1k training done on TPUs thanks to support of the TRC program.

Model Variants in maxxvit.py

MaxxViT covers a number of related model architectures that share a common structure including:

• CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages.
• MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid).
• CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm).
• MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm).
• MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate.

Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string rw are timm specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string tf are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 15.5
  • GMACs: 4.5
  • Activations (M): 30.3

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