---
library_name: litert
base_model: timm/twins_pcpvt_small.in1k
tags:
  - vision
  - image-classification
datasets:
  - imagenet-1k
---
# twins_pcpvt_small

Converted TIMM image classification model for LiteRT.

- Source architecture: `twins_pcpvt_small`
- Source checkpoint: `timm/twins_pcpvt_small.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): 24.1
  - GMACs: 3.8
  - Activations (M): 18.1
  - Image size: 224 x 224
- **Papers:**
  - Twins: Revisiting the Design of Spatial Attention in Vision Transformers: https://arxiv.org/abs/2104.13840
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/Meituan-AutoML/Twins

## Citation

```bibtex
@inproceedings{chu2021Twins,
    title={Twins: Revisiting the Design of Spatial Attention in Vision Transformers},
    author={Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua Shen},
    booktitle={NeurIPS 2021},
    url={https://openreview.net/forum?id=5kTlVBkzSRx},
    year={2021}
}
```