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modified readme.md in psenet

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wsq3 2020-10-12 14:41:44 +08:00
parent f06ce13c7e
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model_zoo/official/cv/psenet/README.md
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@ -57,11 +57,15 @@ After installing MindSpore via the official website, you can start training and
# run distributed training example
sh scripts/run_distribute_train.sh pretrained_model.ckpt
#setup opencv library
download pyblind11, opencv3.4,setup opencv3.4
#download opencv library
download pyblind11, opencv3.4
#make so file
run src/ETSNET/pse/Makefile; make libadaptor.so
#install pyblind11 opencv3.4
setup pyblind11(install the library by the pip command)
setup opencv3.4(compile source code install the library)
#enter the path ,run Makefile to product file
cd ./src/ETSNET/pse/;make
#run test.py
python test.py --ckpt=pretrained_model.ckpt
@ -81,10 +85,10 @@ sh scripts/run_eval_ascend.sh
├── README.md // descriptions about PSENet
├── scripts
├── run_distribute_train.sh // shell script for distributed
└── eval_ic15.sh // shell script for evaluation
└── run_eval_ascend.sh // shell script for evaluation
├── src
├── __init__.py
├── generate_hccn_file.py // creating rank.json
├── generate_hccn_file.py // creating rank.json
├── ETSNET
├── __init__.py
├── base.py // convolution and BN operator
@ -127,7 +131,7 @@ sh scripts/run_distribute_train.sh pretrained_model.ckpt
```
The above shell script will run distribute training in the background. You can view the results through the file
`device[X]/log`. The loss value will be achieved as follows:
`device[X]/test_*.log`. The loss value will be achieved as follows:
```
# grep "epoch: " device_*/loss.log
@ -140,6 +144,8 @@ device_1/log:epcoh: 2, step: 40, loss is 0.76629
```
## [Evaluation Process](#contents)
### run test code
python test.py --ckpt=./device*/ckpt*/ETSNet-*.ckpt
### Eval Script for ICDAR2015
#### Usage
@ -161,7 +167,7 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
| Parameters | PSENet |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | Inception V1 |
| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 09/15/2020 (month/day/year) |
| MindSpore Version | 1.0-alpha |
| Dataset | ICDAR2015 |
@ -187,7 +193,7 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
| MindSpore Version | 1.0-alpha |
| Dataset | ICDAR2015 |
| outputs | probability |
| Accuracy | 1pc: 81%; 8pcs: 81% |
| Accuracy | 1pc: 81%; 4pcs: 81% |
## [How to use](#contents)