---
library_name: litert
base_model: timm/mobilevitv2_050.cvnets_in1k
tags:
  - vision
  - image-classification
datasets:
  - imagenet-1k
---
# mobilevitv2_050

Converted TIMM image classification model for LiteRT.

- Source architecture: `mobilevitv2_050`
- Source checkpoint: `timm/mobilevitv2_050.cvnets_in1k`
- File: `model.tflite`
- Input: `float32` tensor in NCHW layout, shape `[1, 3, 256, 256]`
- 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): 1.4
  - GMACs: 0.5
  - Activations (M): 8.0
  - Image size: 256 x 256
- **Papers:**
  - Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680
- **Original:** https://github.com/apple/ml-cvnets
- **Dataset:** ImageNet-1k

## Citation

```bibtex
@article{Mehta2022SeparableSF,
  title={Separable Self-attention for Mobile Vision Transformers},
  author={Sachin Mehta and Mohammad Rastegari},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.02680}
}
```