deit_small_patch16_224

Converted TIMM image classification model for LiteRT.

• Source architecture: deit_small_patch16_224
• Source checkpoint: timm/deit_small_patch16_224.fb_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): 22.1
  • GMACs: 4.6
  • Activations (M): 11.9
  • Image size: 224 x 224
• Papers:
  • Training data-efficient image transformers & distillation through attention: https://arxiv.org/abs/2012.12877
• Original: https://github.com/facebookresearch/deit
• Dataset: ImageNet-1k

Citation

@InProceedings{pmlr-v139-touvron21a,
  title =     {Training data-efficient image transformers & distillation through attention},
  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
  booktitle = {International Conference on Machine Learning},
  pages =     {10347--10357},
  year =      {2021},
  volume =    {139},
  month =     {July}
}

@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}}
}