README.md

metadata

library_name: litert
base_model: timm/convnext_atto.d2_in1k
tags:
  - vision
  - image-classification
datasets:
  - imagenet-1k

convnext_atto

Converted TIMM image classification model for LiteRT.

• Source architecture: convnext_atto
• Source checkpoint: timm/convnext_atto.d2_in1k
• File: model.tflite
• Input: float32 tensor in NCHW layout, shape [1, 3, 224, 224]
• Output: ImageNet-1K logits, shape [1, 1000]

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 3.7
  • GMACs: 0.6
  • Activations (M): 3.8
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
• Original: https://github.com/huggingface/pytorch-image-models
• Dataset: ImageNet-1k

Citation

@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}}
}

@article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2022},
}