mobilenet_v1

MobileNetv1

MobileNetv1 introduces an efficient backbone that leverages depthwise separable convolutions.

We provide training and evaluation code of MobileNetv1, along with pretrained models and configuration files for image classification on the ImageNet dataset.

Image classification on the ImageNet dataset

Training

To train MobileNetv1-1.0 on ImageNet using a single node with 4 A100 GPUs, run the following command:

export CFG_FILE=projects/mobilenet_v1/classification/mobilenetv1_1.0_in1k.yaml
corenet-train --common.config-file $CFG_FILE --common.results-loc classification_results

We assume that the training and validation data is located at /mnt/imagenet/training and /mnt/imagenet/validation folders, respectively.

Evaluation

To evaluate the pre-trained MobileNetv1-1.0 model on the validation set of the ImageNet, run the following command:

export CFG_FILE=projects/mobilenet_v1/classification/mobilenetv1_1.0_in1k.yaml
export DATASET_PATH="/mnt/vision_datasets/imagenet/validation/" # change to the ImageNet validation path
export MODEL_WEIGHTS=https://docs-assets.developer.apple.com/ml-research/models/cvnets-v2/classification/mobilenetv1-1.00.pt
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=74.044 || top5=91.578

Pretrained Models on ImageNet-1k

Model	Parameters	Top-1	Pretrained weights	Config file	Logs
MobileNetv1-0.25	0.5 M	54.45	Link	Link	Link
MobileNetv1-0.5	1.3 M	65.93	Link	Link	Link
MobileNetv1-0.75	2.6 M	71.44	Link	Link	Link
MobileNetv1-1.00	4.2 M	74.04	Link	Link	Link

Citation

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

@article{Howard2017MobileNetsEC,
  title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
  author={Andrew G. Howard and Menglong Zhu and Bo Chen and Dmitry Kalenichenko and Weijun Wang and Tobias Weyand and Marco Andreetto and Hartwig Adam},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.04861},
  url={https://api.semanticscholar.org/CorpusID:12670695}
}

@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} 
}