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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.

MobileOne Performance

Model Zoo

ImageNet-1K

ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel

*Latency measured on iPhone 12 Pro.

Usage

To use our model, follow the code snippet below,

importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.

importtorchfrommobileoneimportmobileonemodel=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}
}

About

This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.

Resources

Readme
License

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

829 stars

Watchers

16 watching

Forks

66 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

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GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
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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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
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var __re = new RegExp('^' + ".*" + '
GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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})();
})();
GitHub - apple/ml-mobileone: This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023. · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
MobileOne Performance
Model Zoo
ImageNet-1K
ModelTop-1 Acc.Latency*Pytorch Checkpoint (url)CoreML Model
MobileOne-S071.40.79S0(unfused)mlmodel
MobileOne-S175.90.89S1(unfused)mlmodel
MobileOne-S277.41.18S2(unfused)mlmodel
MobileOne-S378.11.53S3(unfused)mlmodel
MobileOne-S479.41.86S4(unfused)mlmodel
*Latency measured on iPhone 12 Pro.
Usage
To use our model, follow the code snippet below,
importtorchfrommobileoneimportmobileone, reparameterize_model# To Train from scratch/fine-tuningmodel=mobileone(variant='s0')
# ... train ...# Load Pre-trained checkpoint for fine-tuningcheckpoint=torch.load('/path/to/unfused_checkpoint.pth.tar')
model.load_state_dict(checkpoint)
# ... train ...# For inferencemodel.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.
importtorchfrommobileoneimportmobileonemodel=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}
}
About
This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone" CVPR 2023.
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
829 stars
Watchers
16 watching
Forks
66 forks
Report repository
Releases
Packages
Used by
Contributors
Languages