# Contents
- [MobileNetV3 Description ](#mobilenetv3-description )
- [Model Architecture ](#model-architecture )
- [Dataset ](#dataset )
- [Environment Requirements ](#environment-requirements )
- [Script Description ](#script-description )
- [Script and Sample Code ](#script-and-sample-code )
- [Training Process ](#training-process )
- [Evaluation Process ](#evaluation-process )
- [Evaluation ](#evaluation )
- [Model Description ](#model-description )
- [Performance ](#performance )
- [Training Performance ](#evaluation-performance )
- [Inference Performance ](#evaluation-performance )
- [Description of Random Situation ](#description-of-random-situation )
- [ModelZoo Homepage ](#modelzoo-homepage )
# [MobileNetV3 Description](#contents)
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 ](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.
# [Model architecture](#contents)
The overall network architecture of MobileNetV3 is show below:
[Link ](https://arxiv.org/pdf/1905.02244 )
# [Dataset](#contents)
Dataset used: [imagenet ](http://www.image-net.org/ )
- 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](#contents)
- Hardware（ ）
- Prepare hardware environment with GPU processor.
- Framework
- [MindSpore ](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/ )
- For more information, please check the resources below：
- [MindSpore tutorials ](https://www.mindspore.cn/tutorial/zh-CN/master/index.html )
- [MindSpore API ](https://www.mindspore.cn/api/zh-CN/master/index.html )
# [Script description](#contents)
## [Script and sample code](#contents)
```python
├── 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](#contents)
### 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](#contents)
### 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](#contents)
## [Performance](#contents)
### 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](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage ](https://gitee.com/mindspore/mindspore/tree/master/model_zoo ).
