---
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

```bibtex
@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}}
}
```
```bibtex
@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},
}        
```
```bibtex
@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}
}
```