mindspore/model_zoo/official/cv/mobilenetv3

• Contents
• MobileNetV3 Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
• • Script and sample code
  • Training process
  • • Usage
    • Launch
    • Result
  • Eval process
  • • Usage
    • Launch
    • Result
• Model description
• • Performance
  • • Training Performance
    • • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• MobileNetV3 Description
• Model Architecture
• Dataset
• Environment Requirements
• Script Description
  • Script and Sample Code
  • Training Process
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Training Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

MobileNetV3 Description

MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.

Paper Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.

Model architecture

The overall network architecture of MobileNetV3 is show below:

Link

Dataset

Dataset used: imagenet

• Dataset size: ~125G, 1.2W colorful images in 1000 classes
  • Train: 120G, 1.2W images
  • Test: 5G, 50000 images
• Data format: RGB images.
  • Note: Data will be processed in src/dataset.py

Environment Requirements

• Hardware（GPU）
  • Prepare hardware environment with GPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore API

Script description

Script and sample code

├── MobileNetV3        
  ├── Readme.md     # descriptions about MobileNetV3                 
  ├── scripts 
  │   ├──run_train.sh   # shell script for train               
  │   ├──run_eval.sh    # shell script for evaluation                
  ├── src                              
  │   ├──config.py      # parameter configuration               
  │   ├──dataset.py     # creating dataset
  │   ├──launch.py      # start python script
  │   ├──lr_generator.py     # learning rate config                            
  │   ├──mobilenetV3.py      # MobileNetV3 architecture
  ├── train.py      # training script
  ├── eval.py       #  evaluation script

Training process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]

Launch

# training example
  python:
      GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
  shell:
      GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/

Result

Training result will be stored in the example path. Checkpoints will be stored at . /checkpoint by default, and training log will be redirected to ./train/train.log like followings.

epoch: [  0/200], step:[  624/  625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917

Eval process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer example
  python:
    GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU

  shell:
    GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, you can find result like the followings in val.log.

result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt

Model description

Performance

Training Performance

Parameters	MobilenetV3
Model Version	large
Resource	NV SMX2 V100-32G
uploaded Date	05/06/2020
MindSpore Version	0.3.0
Dataset	ImageNet
Training Parameters	src/config.py
Optimizer	Momentum
Loss Function	SoftmaxCrossEntropy
outputs
Loss	1.913
Accuracy	ACC1[77.57%] ACC5[92.51%]
Total time
Params (M)
Checkpoint for Fine tuning
Model for inference

Inference Performance

Parameters
Model Version
Resource	NV SMX2 V100-32G
uploaded Date	05/22/2020
MindSpore Version	0.2.0
Dataset	ImageNet, 1.2W
batch_size	130(8P)
outputs
Accuracy	ACC1[75.43%] ACC5[92.51%]
Speed
Total time
Model for inference

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.