swinv2_small_window8_256

swinv2_small_window8_256

Converted TIMM image classification model for LiteRT.

• Source architecture: swinv2_small_window8_256
• Source checkpoint: timm/swinv2_small_window8_256.ms_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): 49.7
  • GMACs: 11.6
  • Activations (M): 40.1
  • 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}}
}