mindcv_poolformer/configs/mobilenetv1

• MobileNetV1
• • Introduction
  • Benchmark
  • Examples
  • • Train
    • Eval

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.

Benchmark

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				Pynative	Pynative	Graph	Graph
	Model	Top-1 (%)	Top-5 (%)	train (s/epoch)	Infer (ms)	train(s/epoch)	Infer (ms)	Download	Config
GPU	MobileNet_v1_100	71.95	90.41					model	config
Ascend	MobileNet_v1_100	71.83	90.26
GPU	MobileNet_v1_075	70.84	89.63					model	config
Ascend	MobileNet_v1_075	70.66	89.49
GPU	MobileNet_v1_050	66.37	86.71					model	config
Ascend	MobileNet_v1_050	66.39	86.85
GPU	MobileNet_v1_025	54.58	78.27					model	config
Ascend	MobileNet_v1_025	54.64	78.29

Examples

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Train

• The yaml config files that yield competitive results on ImageNet for different models are listed in
  the configs folder. To trigger training using preset yaml config.

  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
  mpirun --allow-run-as-root -n 8 python train.py -c configs/mobilenetv1/mobilenetv1_075_gpu.yaml
  

• Here is the example for finetuning a pretrained MobileNetV1 on CIFAR10 dataset using Adam optimizer.

  python train.py --model=mobilenet_v1_075_224 --pretrained --opt=adam --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download
  

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

Eval

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

  python validate.py --model=mobilenet_v1_075_224 --dataset=imagenet --val_split=val --pretrained
  

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

  python validate.py --model=mobilenet_v1_075_224 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/mobilenet_v1_075_224-best.ckpt'