mindcv_fixed/configs/mobilenetv1

• MobileNetV1
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

MobileNetV1

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MobileNetV1: Efficient Convolutional Neural Networks for Mobile Vision Applications

Introduction

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Compared with the traditional convolutional neural network, MobileNetV1's parameters and the amount of computation are
greatly reduced on the premise that the accuracy rate is slightly reduced.
(Compared to VGG16, the accuracy rate is reduced by 0.9%, but the model parameters are only 1/32 of VGG). The model is
based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural
networks.
At the same time, two simple global hyperparameters are introduced, which can effectively trade off latency and
accuracy.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
MobileNet_v1_025	D910x8-G	54.64	78.29	0.47	113s/epoch		model	cfg	log
MobileNet_v1_050	D910x8-G	66.39	86.71	1.34	120s/epoch		model	cfg	log
MobileNet_v1_075	D910x8-G	70.66	89.49	2.60	128s/epoch		model	cfg	log
MobileNet_v1_100	D910x8-G	71.83	90.26	4.25	130s/epoch		model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml files in mindcv/configs/mobilenetv1 folder. For example, to train with one of these configurations, you can run:

  # train densenet121 on 8 GPUs
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  mpirun -n 8 python train.py -c configs/mobilenetv1/mobilenetv1_100_gpu.yaml --data_dir /path/to/imagenet
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascneds with the same batch size.

• Finetuning. Here is an example for finetuning a pretrained mobilenet_050 on CIFAR10 dataset using Momentum optimizer.

  python train.py --model=mobilenet_v1_100_224 --pretrained --opt=momentum --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download
  

Detailed adjustable parameters and their default value can be seen in config.py.

Validation

• To validate the trained model, you can use validate.py. Here is an example for mobilenet_100 to verify the accuracy of
  pretrained weights.

  python validate.py --model=mobilenet_v1_100_224 --dataset=imagenet --val_split=val --pretrained
  

• To validate the model, you can use validate.py. Here is an example for mobilenetv1_100 to verify the accuracy of your
  training.

  python validate.py --model=mobilenet_v1_100_224 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/mobilenet_v1_100_224-200_2502.ckpt'
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.