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model_zoo/official/cv/nasnet/README.md
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# NASNet Example
# Contents
## Description
- [NASNet Description](#nasnet-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [ModelZoo Homepage](#modelzoo-homepage)
This is an example of training NASNet-A-Mobile in MindSpore.
# [NASNet Description](#contents)
## Requirements
- Install [Mindspore](http://www.mindspore.cn/install/en).
- Download the dataset.
[Paper](https://arxiv.org/abs/1707.07012): Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Learning Transferable Architectures for Scalable Image Recognition. 2017.
## Structure
# [Model architecture](#contents)
```shell
The overall network architecture of NASNet is show below:
[Link](https://arxiv.org/abs/1707.07012)
# [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 GPU
- Prepare hardware environment with GPU processor.
- 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
.
└─nasnet
└─nasnet
├─README.md
├─scripts
├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
└─run_eval_for_gpu.sh # launch evaluating with gpu platform
├─scripts
├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
└─run_eval_for_gpu.sh # launch evaluating with gpu platform
├─src
├─config.py # parameter configuration
├─dataset.py # data preprocessing
├─loss.py # Customized CrossEntropy loss function
├─lr_generator.py # learning rate generator
├─nasnet_a_mobile.py # network definition
├─eval.py # eval net
├─export.py # convert checkpoint
└─train.py # train net
├─nasnet_a_mobile.py # network definition
├─eval.py # eval net
├─export.py # convert checkpoint
└─train.py # train net
```
## Parameter Configuration
## [Script Parameters](#contents)
Parameters for both training and evaluating can be set in config.py
Parameters for both training and evaluating can be set in config.py.
```
'random_seed': 1, # fix random seed
@ -56,22 +98,18 @@ Parameters for both training and evaluating can be set in config.py
'opt_eps': 1.0, # epsilon
'rmsprop_decay': 0.9, # rmsprop decay
'loss_scale': 1, # loss scale
```
## Running the example
### Train
## [Training Process](#contents)
#### Usage
```
# distribute training example(8p)
sh run_distribute_train_for_gpu.sh DATA_DIR
# standalone training
sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
GPU:
# distribute training example(8p)
sh run_distribute_train_for_gpu.sh DATA_DIR
# standalone training
sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
```
#### Launch
@ -83,13 +121,11 @@ sh scripts/run_distribute_train_for_gpu.sh /dataset/train
sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
```
#### Result
You can find checkpoint file together with result in log.
### Evaluation
## [Evaluation Process](#contents)
#### Usage
### Usage
```
# Evaluation
@ -103,9 +139,47 @@ sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
sh scripts/run_eval_for_gpu.sh 0 /dataset/val ./checkpoint/nasnet-a-mobile-rank0-248_10009.ckpt
```
> checkpoint can be produced in training process.
#### Result
Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
```
acc=73.5%(TOP1)
```
# [Model description](#contents)
## [Performance](#contents)
### Training Performance
| Parameters | NASNet |
| -------------------------- | ------------------------- |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 09/24/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | src/config.py |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropyWithLogits |
| Loss | 1.8965 |
| Accuracy | 73.5%(TOP1) |
| Total time | 144 h 8ps |
| Checkpoint for Fine tuning | 89 M(.ckpt file) |
### Inference Performance
| Parameters | |
| -------------------------- | ------------------------- |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 09/24/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet, 1.2W |
| batch_size | 32 |
| outputs | probability |
| Accuracy | acc=73.5%(TOP1) |
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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model_zoo/official/cv/shufflenetv2/Readme.md → model_zoo/official/cv/shufflenetv2/README.md
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- [Training Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [ModelZoo Homepage](#modelzoo-homepage)
# [ShuffleNetV2 Description](#contents)
ShuffleNetV2 is a much faster and more accurate netowrk than the previous networks on different platforms such as Ascend or GPU.
@ -117,3 +119,38 @@ You can start evaluation using python or shell scripts. The usage of shell scrip
### Result
Inference result will be stored in the example path, you can find result in `eval.log`.
# [Model description](#contents)
## [Performance](#contents)
### Training Performance
| Parameters | ShuffleNetV2 |
| -------------------------- | ------------------------- |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 09/24/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | src/config.py |
| Optimizer | Momentum |
| Loss Function | CrossEntropySmooth |
| Accuracy | 69.4%(TOP1) |
| Total time | 49 h 8ps |
### Inference Performance
| Parameters | |
| -------------------------- | ------------------------- |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 09/24/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet, 1.2W |
| batch_size | 128 |
| outputs | probability |
| Accuracy | acc=69.4%(TOP1) |
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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