---
license: mit
datasets:
- ILSVRC/imagenet-1k
metrics:
- accuracy
---

# IFORMER: INTEGRATING CONVNET AND TRANS- FORMER FOR MOBILE APPLICATION

<!-- Provide a quick summary of what the model is/does. -->

Official PyTorch implementation of iFormer, published on ICLR 2025.

## Main Results on ImageNet with Pretrained Models
| Model      | Params(M) | GMACs | Latency(ms) | Top-1(%)  |                                                                                             Ckpt.                                                                                              |                                                                                                          Core ML                                                                                                          |                                                                                               Log                                                                                               |
|:-----------|:---------:|:-----:|:-----------:|:---------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| iFormer-T  |    2.9    | 0.53  |    0.60     |   74.1    |                                                     300e(iFormer_t.pth)                                                     |                                                           300e(iFormer_t_224.mlpackage.zip)                                                            |                                                     300e(iFormer_t.out)                                                      |
| iFormer-S  |    6.5    | 1.09  |    0.85     |   78.8    |                                                     300e(iFormer_s.pth)                                                     |                                                           300e(iFormer_s_224.mlpackage.zip)                                                            |                                                     300e(iFormer_s.out)                                                      |
| iFormer-M  |    8.9    | 1.64  |    1.10     | 80.4/81.1 | 300e(iFormer_m.pth)/300e distill(iFormer_m_distill.pth)  |     300e(iFormer_m_224.mlpackage.zip)/300e distill(iFormer_m_224_distill.mlpackage.zip)     | 300e(iFormer_m.out) / 300e distill(iFormer_m_distill.out) |
| iFormer-L  |   14.7    | 2.63  |    1.60     | 81.9/82.7 | 300e(iFormer_l.pth) /300e distill(iFormer_l_distill.pth) | 300e(iFormer_l_224.mlpackage.zip)/300e distill(iFormer_l_224_distill.mlpackage.zip) |                                            300e(iFormer_l.out) /300e distill(iFormer_l_distill.out)                                             |
| iFormer-L2 |   24.5    | 4.50  |    2.30     |   83.9    |                                            300e distill(iFormer_l2_distill.pth)                                             |                                                   300e distill(iFormer_l2_224_distill.mlpackage.zip)                                                   |                                             300e distill(iFormer_l2_distill.out)                                             |
| iFormer-H  |   99.0    | 15.5  |      -      |   84.8    |                                                     300e(iFormer_h.pth)                                                     |                                                           300e(iFormer_h_224.mlpackage.zip)                                                            |                                                     300e(iFormer_h.out)                                                      |
* iFormer-L2 is trained with distillation for 450 epochs.

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->



#### Summary
You can also see [iFormer github](https://github.com/ChuanyangZheng/iFormer) for usage.


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@article{zheng2025iformer,
  title={iFormer: Integrating ConvNet and Transformer for Mobile Application},
  author={Zheng, Chuanyang},
  journal={arXiv preprint arXiv:2501.15369},
  year={2025}
}
```
## Model Card Authors

Chuanyang Zheng

## Model Card Contact

chuanyang zheng@sjtu.edu.cn