README.md

metadata

library_name: litert
base_model: timm/maxvit_tiny_rw_224.sw_in1k
tags:
  - vision
  - image-classification
datasets:
  - imagenet-1k

maxvit_tiny_rw_224

Converted TIMM image classification model for LiteRT.

• Source architecture: maxvit_tiny_rw_224
• Source checkpoint: timm/maxvit_tiny_rw_224.sw_in1k
• File: model.tflite
• Input: float32 tensor in NCHW layout, shape [1, 3, 224, 224]
• Output: ImageNet-1K logits, shape [1, 1000]

Runtime Status

• CPU smoke test: passed with LiteRT CompiledModel.
• GPU delegation: currently blocked for this model by rank-5 tensor patterns in the GPU backend, mostly RESHAPE, TRANSPOSE, and related window/attention operations. The model is published as CPU-ready while GPU support is being improved.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 29.1
  • GMACs: 5.1
  • Activations (M): 33.1
  • Image size: 224 x 224
• Papers:
  • MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697
• Dataset: ImageNet-1k

Citation

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}

@article{tu2022maxvit,
  title={MaxViT: Multi-Axis Vision Transformer},
  author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
  journal={ECCV},
  year={2022},
}        

@article{dai2021coatnet,
  title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
  author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing},
  journal={arXiv preprint arXiv:2106.04803},
  year={2021}
}