提交 ca6da675 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!3218 Move mass and fasterrcnn to new model zoo directory.

Merge pull request !3218 from linqingke/mass
......@@ -139,4 +139,4 @@ Inference result will be stored in the example path, whose folder name is "infer
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631
```
\ No newline at end of file
```
......@@ -548,7 +548,7 @@ Run the shell script `run.sh` as followed:
```bash
sh run.sh -t t -n 1 -i 1 -c /mass/config/config.json
```
Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
## Fine-tuning
For fine-tuning a model, config the options in `config.json` firstly:
......@@ -562,7 +562,7 @@ Run the shell script `run.sh` as followed:
```bash
sh run.sh -t t -n 1 -i 1 -c config/config.json
```
Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
## Inference
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html).
......
......@@ -149,19 +149,19 @@ do
cd $file_path || exit
cd ../ || exit
rm -rf ./run_mass_$DEVICE_ID
mkdir ./run_mass_$DEVICE_ID
rm -rf ./${task}_mass_$DEVICE_ID
mkdir ./${task}_mass_$DEVICE_ID
cp train.py ./run_mass_$DEVICE_ID
cp eval.py ./run_mass_$DEVICE_ID
cp $configurations ./run_mass_$DEVICE_ID
cp train.py ./${task}_mass_$DEVICE_ID
cp eval.py ./${task}_mass_$DEVICE_ID
cp $configurations ./${task}_mass_$DEVICE_ID
if [ $vocab ]
then
cp $vocab ./run_mass_$DEVICE_ID
cp $vocab ./${task}_mass_$DEVICE_ID
fi
cd ./run_mass_$DEVICE_ID || exit
cd ./${task}_mass_$DEVICE_ID || exit
env > log.log
echo $task
if [ "$task" == "train" ]
......
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