# Contents
- [GhostNet Description ](#ghostnet-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 )
# [GhostNet Description](#contents)
The GhostNet architecture is based on an Ghost module structure which generate more features from cheap operations. Based on a set of intrinsic feature maps, a series of cheap operations are applied to generate many ghost feature maps that could fully reveal information underlying intrinsic features.
[Paper ](https://openaccess.thecvf.com/content_CVPR_2020/papers/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.pdf ): Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu. GhostNet: More Features from Cheap Operations. CVPR 2020.
# [Model architecture](#contents)
The overall network architecture of GhostNet is show below:
[Link ](https://openaccess.thecvf.com/content_CVPR_2020/papers/Han_GhostNet_More_Features_From_Cheap_Operations_CVPR_2020_paper.pdf )
# [Dataset](#contents)
Dataset used: [Oxford-IIIT Pet ](https://www.robots.ox.ac.uk/~vgg/data/pets/ )
- Dataset size: 7049 colorful images in 1000 classes
- Train: 3680 images
- Test: 3369 images
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
# [Environment Requirements](#contents)
- Hardware（
- Prepare hardware environment with Ascend or GPU. 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.
- Framework
- [MindSpore ](https://www.mindspore.cn/install/en )
- For more information, please check the resources below：
- [MindSpore Tutorials ](https://www.mindspore.cn/tutorial/training/en/master/index.html )
- [MindSpore Python API ](https://www.mindspore.cn/doc/api_python/en/master/index.html )
# [Script description](#contents)
## [Script and sample code](#contents)
```python
├── GhostNet
├── Readme.md # descriptions about ghostnet # shell script for evaluation with CPU, GPU or Ascend
├── src
│ ├──config.py # parameter configuration
│ ├──dataset.py # creating dataset
│ ├──launch.py # start python script
│ ├──lr_generator.py # learning rate config
│ ├──ghostnet.py # GhostNet architecture
│ ├──ghostnet600.py # GhostNet-600M architecture
├── eval.py # evaluation script
├── mindspore_hub_conf.py # export model for hub
```
## [Training process](#contents)
To Be Done
## [Eval process](#contents)
### Usage
After installing MindSpore via the official website, you can start evaluation as follows:
### Launch
```
# infer example
Ascend: python eval.py --model [ghostnet/ghostnet-600] --dataset_path ~/Pets/test.mindrecord --platform Ascend --checkpoint_path [CHECKPOINT_PATH]
GPU: python eval.py --model [ghostnet/ghostnet-600] --dataset_path ~/Pets/test.mindrecord --platform GPU --checkpoint_path [CHECKPOINT_PATH]
```
> checkpoint can be produced in training process.
### Result
```
result: {'acc': 0.8113927500681385} ckpt= ./ghostnet_nose_1x_pets.ckpt
result: {'acc': 0.824475333878441} ckpt= ./ghostnet_1x_pets.ckpt
result: {'acc': 0.8691741618969746} ckpt= ./ghostnet600M_pets.ckpt
```
# [Model Description](#contents)
## [Performance](#contents)
#### Evaluation Performance
###### GhostNet on ImageNet2012
| Parameters | | |
| -------------------------- | -------------------------------------- |---------------------------------- |
| Model Version | GhostNet |GhostNet-600|
| uploaded Date | 09/08/2020 (month/day/year) ；
| MindSpore Version | 0.6.0-alpha |0.6.0-alpha |
| Dataset | ImageNet2012 | ImageNet2012|
| Parameters (M) | 5.2 | 11.9 |
| FLOPs (M) | 142 | 591 |
| Accuracy (Top1) | 73.9 |80.2 |
###### GhostNet on Oxford-IIIT Pet
| Parameters | | |
| -------------------------- | -------------------------------------- |---------------------------------- |
| Model Version | GhostNet |GhostNet-600|
| uploaded Date | 09/08/2020 (month/day/year) ；
| MindSpore Version | 0.6.0-alpha |0.6.0-alpha |
| Dataset | Oxford-IIIT Pet | Oxford-IIIT Pet|
| Parameters (M) | 3.9 | 10.6 |
| FLOPs (M) | 140 | 590 |
| Accuracy (Top1) | 82.4 |86.9 |
###### Comparison with other methods on Oxford-IIIT Pet
|Model|FLOPs (M)|Latency (ms)*|Accuracy (Top1)|
|-|-|-|-|
|MobileNetV2-1x|300|28.2|78.5|
|Ghost-1x w\o SE|138|19.1|81.1|
|Ghost-1x|140|25.3|82.4|
|Ghost-600|590|-|86.9|
*The latency is measured on Huawei Kirin 990 chip under single-threaded mode with batch size 1.
# [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 ).
