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iFormer
BillionZheng/iFormer
iFormer is a machine learning model from BillionZheng. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Official PyTorch implementation of iFormer, published on ICLR 2025.
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Public
Updated Feb 6, 2025
Repo size
2.8 GB
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.pth2.8 GB · 100%
At a glance
- License
- mit
- Access
- Public
- Created
- Feb 6, 2025
- Updated
- Feb 6, 2025
- SHA
- 2d5fdad5
Datasets
Model card
From the Hugging Face model README
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 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 [email protected]
- License
- mit
- Created
- Feb 6, 2025
- Updated
- Feb 6, 2025