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Update the directory structure of examples
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Signed-off-by: khalid-davis <huangqinkai1@huawei.com>
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khalid-huang authored and llhuii committed Jan 28, 2021
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -74,7 +74,7 @@ Neptune consists of the following components:
Follow the [Neptune installation document](docs/setup/install.md) to install Neptune.

### Examples
Example1:[Joint Inference Service in Helmet Detection Scenario](/examples/helmet_detection_inference/README.md).
Example1:[Joint Inference Service in Helmet Detection Scenario](/examples/joint_inference/helmet_detection_inference/README.md).


## Roadmap
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2 changes: 1 addition & 1 deletion README_zh.md
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Expand Up @@ -62,7 +62,7 @@ Neptune由以下组件构建:
Neptune的安装文档请参考[这里](/docs/setup/install.md)

### 样例
样例1:[大小模型协同推理](/examples/helmet_detection_inference/README.md)
样例1:[大小模型协同推理](/examples/joint_inference/helmet_detection_inference/README.md)


## 路标
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Expand Up @@ -14,14 +14,14 @@ Follow the [Neptune installation document](/docs/setup/install.md) to install Ne

### Prepare Dataset

Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/surface_defect_detection/data/1.txt) to `/data` of edge1.
Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/surface_defect_detection/data/2.txt) to `/data` of edge2.
Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/1.txt) to `/data` of edge1.
Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/2.txt) to `/data` of edge2.

### Prepare Script

Download the script [aggregate.py](/examples/surface_defect_detection/aggregation_worker/aggregate.py) to the `/code` of cloud node.
Download the script [aggregate.py](/examples/federated_learning/surface_defect_detection/aggregation_worker/aggregate.py) to the `/code` of cloud node.

Download the script [training_worker](/examples/surface_defect_detection/training_worker/train.py) to the `/code` of edge1 and edge2.
Download the script [training_worker](/examples/federated_learning/surface_defect_detection/training_worker/train.py) to the `/code` of edge1 and edge2.


### Create Federated Learning Job
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Expand Up @@ -39,7 +39,7 @@ wget https://edgeai-neptune.obs.cn-north-1.myhuaweicloud.com/examples/helmet-det
tar -zxvf model.tar.gz
```
### Prepare Script
Download the [scripts](/examples/helmet_detection_incremental_train/training) to the path `code` of your node
Download the [scripts](/examples/incremental_learning/helmet_detection_incremental_train/training) to the path `code` of your node


### Create Incremental Job
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Expand Up @@ -33,14 +33,14 @@ tar -zxvf big-model.tar.gz
```

### Prepare Script
* step1: download the script [little_model.py](/examples/helmet_detection_inference/little_model/little_model.py) to the path `/code/little_model` of edge node.
* step1: download the script [little_model.py](/examples/joint_inference/helmet_detection_inference/little_model/little_model.py) to the path `/code/little_model` of edge node.

```
mkdir -p /code/little_model
curl -o little_model.py https://github.com/edgeai-neptune/neptune/blob/main/examples/helmet_detection_inference/little_model/little_model.py
```

* step2: download the script [big_model.py](/examples/helmet_detection_inference/big_model/big_model.py) to the path `/code/big_model` of cloud node.
* step2: download the script [big_model.py](/examples/joint_inference/helmet_detection_inference/big_model/big_model.py) to the path `/code/big_model` of cloud node.

```
mkdir -p /code/big_model
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#### Create JointInferenceService

Note the setting of the following parameters, which have to same as the script [little_model.py](/examples/helmet_detection_inference/little_model/little_model.py):
Note the setting of the following parameters, which have to same as the script [little_model.py](/examples/joint_inference/helmet_detection_inference/little_model/little_model.py):
- hardExampleMining: set hard example algorithm from {IBT, CrossEntropy} for inferring in edge side.
- video_url: set the url for video streaming.
- all_examples_inference_output: set your output path for the inference results, and note that the root path has to be /home/data.
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