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# Contents
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- [MobileNetV3 Description](#mobilenetv3-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Export MindIR](#export-mindir)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#evaluation-performance)
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- [Inference Performance](#evaluation-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [MobileNetV3 Description](#contents)
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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.
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[Paper](https://arxiv.org/pdf/1905.02244) 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.
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# [Model architecture](#contents)
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The overall network architecture of MobileNetV3 is show below:
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[Link](https://arxiv.org/pdf/1905.02244)
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# [Dataset](#contents)
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Dataset used: [imagenet](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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# [Environment Requirements](#contents)
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- Hardware（GPU）
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- Prepare hardware environment with GPU processor.
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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# [Script description](#contents)
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## [Script and sample code](#contents)
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```python
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├── MobileNetV3
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├── Readme.md # descriptions about MobileNetV3
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├── scripts
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│ ├──run_train.sh # shell script for train
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│ ├──run_eval.sh # shell script for evaluation
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├── src
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│ ├──config.py # parameter configuration
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│ ├──dataset.py # creating dataset
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│ ├──lr_generator.py # learning rate config
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│ ├──mobilenetV3.py # MobileNetV3 architecture
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├── train.py # training script
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├── eval.py # evaluation script
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├── export.py # export mindir script
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├── mindspore_hub_conf.py # mindspore hub interface
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```
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## [Training process](#contents)
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### Usage
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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### Launch
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```
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# training example
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python:
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GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
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shell:
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
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```
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### Result
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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.
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```
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epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
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epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
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epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
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epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
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```
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## [Eval process](#contents)
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### Usage
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
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### Launch
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```
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# infer example
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python:
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GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
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shell:
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GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
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```
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> checkpoint can be produced in training process.
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### Result
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Inference result will be stored in the example path, you can find result like the followings in `val.log`.
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```
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result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
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```
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## [Export MindIR](#contents)
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Change the export mode and export file in `src/config.py`, and run `export.py`.
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```
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python export.py --device_target [PLATFORM] --checkpoint_path [CKPT_PATH]
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```
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# [Model description](#contents)
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## [Performance](#contents)
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### Training Performance
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| Parameters | MobilenetV3 |
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| -------------------------- | ------------------------- |
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| Model Version | large |
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| Resource | NV SMX2 V100-32G |
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| uploaded Date | 05/06/2020 |
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| MindSpore Version | 0.3.0 |
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| Dataset | ImageNet |
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| Training Parameters | src/config.py |
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| Optimizer | Momentum |
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| Loss Function | SoftmaxCrossEntropy |
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| outputs | probability |
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| Loss | 1.913 |
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| Accuracy | ACC1[77.57%] ACC5[92.51%] |
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| Total time | 1433 min |
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| Params (M) | 5.48 M |
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| Checkpoint for Fine tuning | 44 M |
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| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv3)|
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# [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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