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Updated Ianvs QuickStart guide by removing conflicts
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quickStart guide updated

Signed-off-by: Aryan Nanda <[email protected]>

README.md added to examples/pcb-aoi/singletask_learning_bench/fault_detection

Signed-off-by: Aryan Nanda <[email protected]>
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AryanNanda17 committed Jan 11, 2025
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43 changes: 22 additions & 21 deletions docs/guides/quick-start.md
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Expand Up @@ -24,19 +24,15 @@ Before using Ianvs, you might want to have the device ready:
- Internet connection for GitHub and pip, etc
- Python 3.6+ installed

In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few commands and package requirements might be different.
In this example, we are using the Linux platform with **Python 3.6.9**. If you are using Windows, most steps should still apply but a few commands and package requirements might be different.

## Step 1. Ianvs Preparation

First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git`
as:
First, we download the code of Ianvs. Assuming that we are using the `home` directory as the workspace, Ianvs can be cloned with Git as:

``` shell
mkdir /ianvs
cd /ianvs #One might use another path preferred
cd ~

mkdir project
cd project
git clone https://github.com/kubeedge/ianvs.git
```

Expand All @@ -47,9 +43,10 @@ sudo apt-get update
sudo apt-get install libgl1-mesa-glx -y
python -m pip install --upgrade pip

cd ianvs
cd ~/ianvs
python -m pip install ./examples/resources/third_party/*
python -m pip install -r requirements.txt
python -m pip install -r ./examples/pcb-aoi/singletask_learning_bench/fault_detection/requirements.txt
```

We are now ready to install Ianvs.
Expand All @@ -65,7 +62,7 @@ If you want to use a separate space to do work, you may select the following met
python -m pip install --pre envd
envd bootstrap

cd /ianvs/project/ianvs
cd ~/ianvs
envd build build.envd
envd up
```
Expand All @@ -84,7 +81,7 @@ this document, we are using [the PCB-AoI Public Dataset] put on Kaggle. The data
See [Details of PCB-AoI dataset] for more information.

``` shell
cd /ianvs #One might use another path preferred
cd ~/ianvs #One might use another path preferred
mkdir dataset
cd dataset
wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com:443/ianvs/pcb-aoi/dataset.zip
Expand All @@ -94,24 +91,24 @@ unzip dataset.zip
The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start,
we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details.

<!-- Please put the downloaded dataset on the above dataset path, e.g., `/ianvs/dataset`. One can transfer the dataset to the path, e.g., on a remote Linux system using [XFTP]. -->
<!-- Please put the downloaded dataset on the above dataset path, e.g., `~/ianvs/dataset`. One can transfer the dataset to the path, e.g., on a remote Linux system using [XFTP]. -->

Then we may Develop the targeted algorithm as usual. In this quick start, Ianvs has prepared an initial model for
benchmarking. One can find the model at [FPN-model].

``` shell
cd /ianvs #One might use another path preferred
cd ~/ianvs #One might use another path preferred
mkdir initial_model
cd initial_model
wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com:443/ianvs/pcb-aoi/model.zip
```

<!-- Please put the downloaded model on the above model path, e.g., `/ianvs/initial_model`. One can transfer the model to the path, e.g., on remote a Linux system using [XFTP]. -->
<!-- Please put the downloaded model on the above model path, e.g., `~/ianvs/initial_model`. One can transfer the model to the path, e.g., on remote a Linux system using [XFTP]. -->

Related algorithm is also ready as a wheel in this quick start.

``` shell
cd /ianvs/project/ianvs/
cd ~/ianvs/
python -m pip install examples/resources/algorithms/FPN_TensorFlow-0.1-py3-none-any.whl
```

Expand All @@ -123,20 +120,24 @@ start, we have done that for you and the interested readers can refer to the [al
We are now ready to run the ianvs for benchmarking on the PCB-AoI dataset.

``` shell
ianvs -f ./examples/pcb-aoi/singletask_learning_bench/benchmarkingjob.yaml
ianvs -f ./examples/pcb-aoi/singletask_learning_bench/fault_detection/benchmarkingjob.yaml
```

Finally, the user can check the result of benchmarking on the console and also in the output path(
e.g. `/ianvs/singletask_learning_bench/workspace`) defined in the benchmarking config file (
e.g. `~/ianvs/workspace`) defined in the benchmarking config file (
e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers
can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details.

The final output might look like this:

|rank |algorithm |f1_score |paradigm |basemodel |learning_rate |momentum |time |url |
|:----:|:-----------------------:|:--------:|:------------------:|:---------:|:-------------:|:--------:|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
|1 |fpn_singletask_learning | 0.8396 |singletasklearning | FPN | 0.1 | 0.5 | 2022-07-07 20:33:53 |/ianvs/pcb-aoi/singletask_learning_bench/workspace/benchmarkingjob/fpn_singletask_learning/49eb5ffd-fdf0-11ec-8d5d-fa163eaa99d5 |
|2 |fpn_singletask_learning | 0.8353 |singletasklearning | FPN | 0.1 | 0.95 | 2022-07-07 20:31:08 |/ianvs/pcb-aoi/singletask_learning_bench/workspace/benchmarkingjob/fpn_singletask_learning/49eb5ffc-fdf0-11ec-8d5d-fa163eaa99d5 |
```bash
+------+-------------------------+----------+--------------------+-----------+--------------------+-------------------------+---------------------+------------------------------------------------------------------------------------------+
| rank | algorithm | f1_score | paradigm | basemodel | basemodel-momentum | basemodel-learning_rate | time | url |
+------+-------------------------+----------+--------------------+-----------+--------------------+-------------------------+---------------------+------------------------------------------------------------------------------------------+
| 1 | fpn_singletask_learning | 0.8527 | singletasklearning | FPN | 0.5 | 0.1 | 2025-01-06 14:30:30 | ./workspace/benchmarkingjob/fpn_singletask_learning/3a76bc25-cc0b-11ef-9f00-65cc74a7c013 |
| 2 | fpn_singletask_learning | 0.844 | singletasklearning | FPN | 0.95 | 0.1 | 2025-01-06 14:25:18 | ./workspace/benchmarkingjob/fpn_singletask_learning/3a76bc24-cc0b-11ef-9f00-65cc74a7c013 |
+------+-------------------------+----------+--------------------+-----------+--------------------+-------------------------+---------------------+------------------------------------------------------------------------------------------+
```

This ends the quick start experiment.

Expand All @@ -156,4 +157,4 @@ If the reader is ready to explore more on Ianvs, e.g., after the quick start, th

If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue.

Enjoy your journey on Ianvs!
Enjoy your journey on Ianvs!
1 change: 1 addition & 0 deletions examples/pcb-aoi/singletask_learning_bench/fault detection/README.md
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- To execute the `examples/pcb-aoi/singletask_learning_bench/fault_detection` example, refer to the [Ianvs Quick Start Guide](https://ianvs.readthedocs.io/en/latest/guides/quick-start.html).
2 changes: 2 additions & 0 deletions examples/pcb-aoi/singletask_learning_bench/fault detection/requirements.txt
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opencv-python~=3.4 # Must be under 4.0, otherwise it will build from source, which takes a long time
onnx==1.9.0

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