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 | 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.
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-timeTo 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 ...An iOS benchmark app for MobileOne CoreML models. See ModelBench for addition details on building and running the app.
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}
}