---
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

```bibtex
@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}}
}
```
```bibtex
@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},
}
```