metadata
tags:
- image-classification
library_name: coreml
license: other
license_name: apple-ascl
license_link: LICENSE
datasets:
- imagenet-1k
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Please observe original license.
Model Details
- Model Type: Image classification
- Model Stats:
- Params (M): 44.1
- GMACs: 7.8
- Activations (M): 40.4
- Image size: 256 x 256
- Papers:
- FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization: https://arxiv.org/abs/2303.14189
- Original: https://github.com/apple/ml-fastvit
- Dataset: ImageNet-1k
Evaluation - Variants
| Variant | Parameters | Size (MB) | Weight precision | Act. precision | Δ Pytorch acc |
|---|---|---|---|---|---|
| T8 | 3.6M | 7.8 | Float16 | Float16 | -0.9% |
| MA36 | 42.7M | 84 | Float16 | Float16 | -0.06% |
Evaluaition - Inference time
| Variant | Device | OS | Inference time (ms) | Dominant compute unit |
|---|---|---|---|---|
| T8 | iPhone 12 Pro Max | 17.5 | 0.79 | Neural Engine |
| T8 | M3 Max | 14.4 | 0.62 | Neural Engine |
| MA36 | iPhone 12 Pro Max | 18.0 | 4.50 | Neural Engine |
| MA36 | M3 Max | 15.0 | 2.99 | Neural Engine |
Citation
@inproceedings{vasufastvit2023,
author = {Pavan Kumar Anasosalu Vasu and James Gabriel and Jeff Zhu and Oncel Tuzel and Anurag Ranjan},
title = {FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2023}
}