swinv2_tiny_window8_256

Converted TIMM image classification model for LiteRT.

• Source architecture: swinv2_tiny_window8_256
• FP32 file: model.tflite
• Static INT8 file: model_static_int8.tflite

Compatibility

File	CPU	GPU	NPU
model.tflite	Supported	Not supported	N/A
model_static_int8.tflite	Supported	Not supported	Qualcomm

The NPU entry denotes compilation support; runtime accuracy is unverified.

model_static_int8.tflite uses INT8 inputs/outputs and convolution/FC quantization; attention, normalization and other operations remain FP32. Apply the source checkpoint’s preprocessing, then quantize the input and dequantize the output using the file’s tensor scales and zero points.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 28.3
  • GMACs: 6.0
  • Activations (M): 24.6
  • Image size: 256 x 256
• Papers:
  • Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
• Original: https://github.com/microsoft/Swin-Transformer
• Dataset: ImageNet-1k

Citation

@inproceedings{liu2021swinv2,
  title={Swin Transformer V2: Scaling Up Capacity and Resolution}, 
  author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
  booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

@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}}
}