Awesome
MobileOne: An Improved One millisecond Mobile Backbone
This software project accompanies the research paper, An Improved One millisecond Mobile Backbone.
Our model achieves Top-1 Accuracy of 75.9% under 1ms.
Model Zoo
ImageNet-1K
| Model | Top-1 Acc. | Latency* | Pytorch Checkpoint (url) | CoreML Model |
|---|---|---|---|---|
| MobileOne-S0 | 71.4 | 0.79 | S0(unfused) | mlmodel |
| MobileOne-S1 | 75.9 | 0.89 | S1(unfused) | mlmodel |
| MobileOne-S2 | 77.4 | 1.18 | S2(unfused) | mlmodel |
| MobileOne-S3 | 78.1 | 1.53 | S3(unfused) | mlmodel |
| MobileOne-S4 | 79.4 | 1.86 | S4(unfused) | mlmodel |
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
import torch
from mobileone import mobileone, reparameterize_model
# To Train from scratch/fine-tuning
model = mobileone(variant='s0')
# ... train ...
# Load Pre-trained checkpoint for fine-tuning
checkpoint = torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...
# For inference
model.eval()
model_eval = reparameterize_model(model)
# Use model_eval at test-time
To simply evaluate our model, use the fused checkpoint where branches are re-parameterized.
import torch
from mobileone import mobileone
model = mobileone(variant='s0', inference_mode=True)
checkpoint = torch.load('/path/to/checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... evaluate/demo ...
ModelBench App
An iOS benchmark app for MobileOne CoreML models. See ModelBench for addition details on building and running the app.
Citation
If our code or models help your work, please cite our paper:
@article{mobileone2022,
title={An Improved One millisecond Mobile Backbone},
author={Vasu, Pavan Kumar Anasosalu and Gabriel, James and Zhu, Jeff and Tuzel, Oncel and Ranjan, Anurag},
journal={arXiv preprint arXiv:2206.04040},
year={2022}
}