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# MobileNetV3 Description
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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
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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
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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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# Features
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# Environment Requirements
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- Hardware（Ascend/GPU）
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- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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# Script description
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## Script and sample code
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```python
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├── MobilenetV3
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├── Readme.md
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├── scripts
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│ ├──run_train.sh
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│ ├──run_eval.sh
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├── src
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│ ├──config.py
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│ ├──dataset.py
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│ ├──luanch.py
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│ ├──lr_generator.py
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│ ├──mobilenetV2.py
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├── train.py
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├── eval.py
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```
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## Training process
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### Usage
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- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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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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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
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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
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### Usage
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- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
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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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Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
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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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# Model description
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## Performance
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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 | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
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| uploaded Date | 05/06/2020 | 05/06/2020 |
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| MindSpore Version | 0.3.0 | 0.3.0 |
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| Dataset | ImageNet | ImageNet |
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| Training Parameters | src/config.py | src/config.py |
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| Optimizer | Momentum | Momentum |
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| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
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| outputs | | |
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| Loss | | 1.913 |
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| Accuracy | | ACC1[77.57%] ACC5[92.51%] |
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| Total time | | |
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| Params (M) | | |
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| Checkpoint for Fine tuning | | |
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| Model for inference | | |
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#### Inference Performance
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| Parameters | | | |
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| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
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| Model Version | V1 | | |
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| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 |
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| uploaded Date | 05/06/2020 | 05/22/2020 | |
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| MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 |
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| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |
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| batch_size | | 130(8P) | |
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| outputs | | | |
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| Accuracy | | ACC1[75.43%] ACC5[92.51%] | |
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| Speed | | | |
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| Total time | | | |
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| Model for inference | | | |
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# ModelZoo Homepage
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[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo) |
