MobileOne: An Improved One millisecond Mobile Backbone

MobileOne is an efficient CNN architecture that attains SOTA accuracy to latency tradeoff.

We provide training and evaluation code of MobileOne, along with pretrained models and configuration files for the following tasks:

ImageNet classification

Training

Single node training with 4 A100 GPUs of MobileOne-S1 model can be done using below command:

export CFG_FILE="projects/mobileone/classification/mobileone_s1_in1k.yaml"
corenet-train --common.config-file $CFG_FILE --common.results-loc classification_results

Note: Do not forget to change the training and validation dataset locations in configuration files.

Evaluation

We evaluate the model on a single GPU using following command:

 export MODEL_WEIGHTS="https://docs-assets.developer.apple.com/ml-research/models/corenet/v0.1.0/mobileone/imagenet-1k/mobileone-s1.pt"
 export CFG_FILE="projects/mobileone/classification/mobileone_s1_in1k.yaml"
 export DATASET_PATH="/mnt/vision_datasets/imagenet/validation/" # change to the ImageNet validation path
 CUDA_VISIBLE_DEVICES=0 corenet-eval --common.config-file $CFG_FILE --model.classification.pretrained $MODEL_WEIGHTS --common.override-kwargs dataset.root_val=$DATASET_PATH

This should give:

top1=75.316 || top5=92.544

Citation

If you find our work useful, please cite following papers:

@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}
}

@inproceedings{mehta2022cvnets,
     author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
     title = {CVNets: High Performance Library for Computer Vision},
     year = {2022},
     booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
     series = {MM '22}
}