forked from mindspore-Ecosystem/mindspore
!15305 update psenet readme file
From: @anzhengqi Reviewed-by: @heleiwang,@pandoublefeng Signed-off-by: @pandoublefeng
This commit is contained in:
Gitee
commit
af8b2d3a47
|
|
@ -90,31 +90,34 @@ sh scripts/run_eval_ascend.sh
|
|||
|
||||
```path
|
||||
└── PSENet
|
||||
├── README.md // descriptions about PSENet
|
||||
├── export.py // export mindir file
|
||||
├── __init__.py
|
||||
├── mindspore_hub_conf.py // hub config file
|
||||
├── README_CN.md // descriptions about PSENet in Chinese
|
||||
├── README.md // descriptions about PSENet in English
|
||||
├── scripts
|
||||
├── run_distribute_train.sh // shell script for distributed
|
||||
└── run_eval_ascend.sh // shell script for evaluation
|
||||
├──src
|
||||
├── __init__.py
|
||||
├── src
|
||||
├── config.py // parameter configuration
|
||||
├── dataset.py // creating dataset
|
||||
├── ETSNET
|
||||
├── __init__.py
|
||||
├── base.py // convolution and BN operator
|
||||
├── dice_loss.py // calculate PSENet loss value
|
||||
├── etsnet.py // Subnet in PSENet
|
||||
├── fpn.py // Subnet in PSENet
|
||||
├── resnet50.py // Subnet in PSENet
|
||||
├── pse // Subnet in PSENet
|
||||
├── etsnet.py // Subnet in PSENet
|
||||
├── fpn.py // Subnet in PSENet
|
||||
├── __init__.py
|
||||
├── pse // Subnet in PSENet
|
||||
├── __init__.py
|
||||
├── adaptor.cpp
|
||||
├── adaptor.h
|
||||
├── Makefile
|
||||
├──config.py // parameter configuration
|
||||
├──dataset.py // creating dataset
|
||||
├──network_define.py // learning ratio generation
|
||||
├──export.py // export mindir file
|
||||
├──mindspore_hub_conf.py // hub config file
|
||||
├──test.py // test script
|
||||
├──train.py // training script
|
||||
├── resnet50.py // Subnet in PSENet
|
||||
├── __init__.py
|
||||
├── lr_schedule.py // define learning rate
|
||||
├── network_define.py // learning ratio generation
|
||||
├── test.py // test script
|
||||
├── train.py // training script
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -164,7 +167,9 @@ python test.py --ckpt=./device*/ckpt*/ETSNet-*.ckpt
|
|||
#### Usage
|
||||
|
||||
step 1: download eval method from [here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization).
|
||||
|
||||
step 2: click "My Methods" button,then download Evaluation Scripts.
|
||||
|
||||
step 3: it is recommended to symlink the eval method root to $MINDSPORE/model_zoo/psenet/eval_ic15/. if your folder structure is different,you may need to change the corresponding paths in eval script files.
|
||||
|
||||
```shell
|
||||
|
|
@ -181,12 +186,12 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
|
|||
|
||||
### Evaluation Performance
|
||||
|
||||
| Parameters | PSENet |
|
||||
| Parameters | Ascend |
|
||||
| -------------------------- | ----------------------------------------------------------- |
|
||||
| Model Version | V1 |
|
||||
| Model Version | PSENet |
|
||||
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 |
|
||||
| uploaded Date | 09/30/2020 (month/day/year) |
|
||||
| MindSpore Version | 1.0.0 |
|
||||
| MindSpore Version | 1.0.0 |
|
||||
| Dataset | ICDAR2015 |
|
||||
| Training Parameters | start_lr=0.1; lr_scale=0.1 |
|
||||
| Optimizer | SGD |
|
||||
|
|
@ -194,19 +199,19 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
|
|||
| outputs | probability |
|
||||
| Loss | 0.35 |
|
||||
| Speed | 1pc: 444 ms/step; 8pcs: 446 ms/step |
|
||||
| Total time | 1pc: 75.48 h; 8pcs: 10.01 h |
|
||||
| Total time | 1pc: 75.48 h; 8pcs: 7.11 h |
|
||||
| Parameters (M) | 27.36 |
|
||||
| Checkpoint for Fine tuning | 109.44M (.ckpt file) |
|
||||
| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
|
||||
|
||||
### Inference Performance
|
||||
|
||||
| Parameters | PSENet |
|
||||
| Parameters | Ascend |
|
||||
| ------------------- | --------------------------- |
|
||||
| Model Version | V1 |
|
||||
| Model Version | PSENet |
|
||||
| Resource | Ascend 910 |
|
||||
| Uploaded Date | 09/30/2020 (month/day/year) |
|
||||
| MindSpore Version | 1.0,0 |
|
||||
| MindSpore Version | 1.0.0 |
|
||||
| Dataset | ICDAR2015 |
|
||||
| outputs | probability |
|
||||
| Accuracy | 1pc: 81%; 8pcs: 81% |
|
||||
|
|
|
|||
|
|
@ -60,7 +60,7 @@
|
|||
|
||||
# 快速入门
|
||||
|
||||
通过官方网站安装MindSpore后，您可以按照如下步骤进行培训和评估：
|
||||
通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
|
||||
|
||||
```python
|
||||
# 分布式训练运行示例
|
||||
|
|
@ -91,31 +91,34 @@ sh scripts/run_eval_ascend.sh
|
|||
## 脚本和样例代码
|
||||
|
||||
```path
|
||||
└── PSENet
|
||||
├── README.md // PSENet相关描述
|
||||
├── scripts
|
||||
└── PSENet
|
||||
├── export.py // mindir转换脚本
|
||||
├── mindspore_hub_conf.py // 网络模型
|
||||
├── README.md // PSENet相关描述英文版
|
||||
├── README_CN.md // PSENet相关描述中文版
|
||||
├── scripts
|
||||
├── run_distribute_train.sh // 用于分布式训练的shell脚本
|
||||
└── run_eval_ascend.sh // 用于评估的shell脚本
|
||||
├──src
|
||||
├── __init__.py
|
||||
├── generate_hccn_file.py // 创建rank.json文件
|
||||
├── ETSNET
|
||||
├── __init__.py
|
||||
├── src
|
||||
├── config.py // 参数配置
|
||||
├── dataset.py // 创建数据集
|
||||
├── ETSNET
|
||||
├── base.py // 卷积和BN算子
|
||||
├── dice_loss.py // 计算PSENet损耗值
|
||||
├── etsnet.py // PSENet中的子网
|
||||
├── fpn.py // PSENet中的子网
|
||||
├── resnet50.py // PSENet中的子网
|
||||
├── __init__.py
|
||||
├── pse // PSENet中的子网
|
||||
├── __init__.py
|
||||
├── adaptor.cpp
|
||||
├── adaptor.h
|
||||
├── __init__.py
|
||||
├── Makefile
|
||||
├──config.py // 参数配置
|
||||
├──dataset.py // 创建数据集
|
||||
├──network_define.py // PSENet架构
|
||||
├──test.py // 测试脚本
|
||||
├──train.py // 训练脚本
|
||||
├── resnet50 // PSENet中的子网
|
||||
├── __init__.py
|
||||
├── lr_schedule.py // 学习率
|
||||
├── network_define.py // PSENet架构
|
||||
├── test.py // 测试脚本
|
||||
├── train.py // 训练脚本
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -162,7 +165,9 @@ python test.py --ckpt=./device*/ckpt*/ETSNet-*.ckpt
|
|||
#### 用法
|
||||
|
||||
第一步：单击[此处](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)下载评估方法。
|
||||
|
||||
第二步：单击"我的方法"按钮，下载评估脚本。
|
||||
|
||||
第三步：建议将评估方法根符号链接到$MINDSPORE/model_zoo/psenet/eval_ic15/。如果您的文件夹结构不同，您可能需要更改评估脚本文件中的相应路径。
|
||||
|
||||
```shell
|
||||
|
|
@ -179,35 +184,35 @@ Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"h
|
|||
|
||||
### 评估性能
|
||||
|
||||
| 参数 | PSENet |
|
||||
| 参数 | Ascend |
|
||||
| -------------------------- | ----------------------------------------------------------- |
|
||||
| 模型版本 | Inception V1 |
|
||||
| 模型版本 | PSENet |
|
||||
| 资源 | Ascend 910； CPU 2.60GHz，192内核；内存 755G；系统 Euler2.8 |
|
||||
| 上传日期 | 2020-09-15 |
|
||||
| MindSpore版本 | 1.0-alpha |
|
||||
| MindSpore版本 | 1.0.0 |
|
||||
| 数据集 | ICDAR2015 |
|
||||
| 训练参数 | start_lr=0.1; lr_scale=0.1 |
|
||||
| 优化器 | SGD |
|
||||
| 损失函数 | LossCallBack |
|
||||
| 输出 | 概率 |
|
||||
| 损失 | 0.35 |
|
||||
| 速度 | 1卡：444毫秒/步；4卡：446毫秒/步
|
||||
| 总时间 | 1卡：75.48小时；4卡：18.87小时|
|
||||
| 速度 | 1卡：444毫秒/步；8卡：446毫秒/步
|
||||
| 总时间 | 1卡：75.48小时；8卡：7.11小时|
|
||||
| 参数(M) | 27.36 |
|
||||
| 微调检查点 | 109.44M （.ckpt file） |
|
||||
| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
|
||||
|
||||
### 推理性能
|
||||
|
||||
| 参数 | PSENet |
|
||||
| 参数 | Ascend |
|
||||
| ------------------- | --------------------------- |
|
||||
| 模型版本 | Inception V1 |
|
||||
| 模型版本 | PSENet |
|
||||
| 资源 | Ascend 910；系统 Euler2.8 |
|
||||
| 上传日期 | 2020/09/15 |
|
||||
| MindSpore版本 | 1.0-alpha |
|
||||
| MindSpore版本 | 1.0.0 |
|
||||
| 数据集| ICDAR2015 |
|
||||
| 输出 | 概率 |
|
||||
| 准确性 | 1卡：81%; 4卡：81% |
|
||||
| 准确性 | 1卡：81%; 8卡：81% |
|
||||
|
||||
## 使用方法
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue