Label Dataset by Label Studio
Last updated
Last updated
This tutorial covers the basic flow to help you get started with Label Studio in PrimeHub.
First, you need to install it in the Apps tab. Please check the Overview section to learn how to install an App.
In the installing process, you can change the environment variables.
DEFAULT_USERNAME and DEFAULT_PASSWORD are the login account information. You can change them and use them to log into Label Studio after installed.
If you don't know the meaning of other environment variables, you can use the default values or check the Label Studio Official Doc or the tooltip beside the environment variable for more details.
PrimeHub shows the app's state in the Apps tab. You can open the Label Studio UI by clicking Open after the state becomes Ready.
It will open a new window and show the Label Studio UI. You can find your login information by clicking Manage in the Apps tab and then clicking the eyes icon. The $(PRIMEHUB_GROUP) is the group name.
The dataset in PrimeHub you want to label (we use /datasets/dog-demo in this tutorial)
The directory in group volume that you want to save the labeled results (we use /project/<group_name>/dog-demo-labeled in the tutorial)
Please have the data volume, group volume, or request administrators for assistance before we start.
After login, please click Create button.
Enter your Project Name. Skip the Data Import step. And choose the Labeling Setup. Here we choose Semantic Segmentation with Polygons.
Delete the original Labels settings and Add our own label names.
Sync the data file folder with label studio.
Click the Settings on the upper-right.
Click Cloud Storage and Add Source Storage to sync the data volume to label
Configure the source storage setting:
| Variable | Value |
|---|---|
Storage type | Local path |
Absolute local path | /datasets/dog-demo/ |
File Filter Regex | .*jpeg |
Treat every bucket object as a source file | Enable |
Click the Sync Storage to sync the data volume
Click Add Target Storage to sync to labeled results to /project/<group_name>/dog-demo-labeled. You need to set Local path to /project/<group_name>/dog-demo-labeled.
Back to the project in Label Studio. The data in the data volume has been shown on the UI. And you can click each row of data to label.
After you submit the labeled result, the labeled json file will be under the /project/<group_name>/dog-demo-labeled.
That's the basic use of how to label the dataset by using Label Studio and PrimeHub. Enjoy it!
In the last section, we show you how to label the dataset. Now, we want to demonstrate how you can use the labeled data to train a model.
For simplicity, the model will be a classification model and you also only need to label the class of the image. The model classifies whether the screw is good or bad.
Here are examples of good and bad screws. The first image is the good screw. The second image is the bad screw and you can see the there is a manipulated front.
Create a data volume in PrimeHub called screw, and set the read/write permission to your group. Please download the app_tutorial_labelstudio_screw_dataset.zip, unzip it and upload images to the ~/datasets/screw folder by the notebook
Create a directory /project/<group_name>/screw-labeled in group volume to save the labeled results
The image infuseai/docker-stacks:pytorch-notebook-v1-7-0-04b2c51f
An instance type >= minimal requirement (CPU=1, GPU=0, Mem=2G)
The prepared python file of the example app_tutorial_labelstudio_screw_prepare.py and upload it to ~/screw_train by the notebook
The prepared notebook file of the example app_tutorial_labelstudio_screw_train.ipynb and upload it to ~/screw_train by the notebook
Please have the data volume, group volume, or request administrators for assistance before we start.
To use the new data volume, you need to create a label studio app after the creation of the data volume.
Follow the previous Label Dataset section to use the label studio. This time in Labeling Setup, we should choose Image Classification.
Delete the original Labels settings and Add our own label classes: bad, good.
Click the Settings on the upper-right. Click Cloud Storage and Add Source Storage to sync the /datasets/screw data volume to label. Set Local path to /datasets/screw, set File Filter Regex to .*png, turn on toggle of Treat every bucket object as a source file. After added, click Sync Storage.
Click Add Target Storage to sync to labeled results to /project/<group_name>/screw-labeled. You need to set Local path to /project/<group_name>/screw-labeled.
Back to the project in Label Studio. The data in the data volume has been shown on the UI. And you can click Label to start labeling. (Tip: you can use number to select the class)
After you labeled all images, you may see the following message. This is a known issue. Please click OK, click your project name and refresh the page.
Now you have labeled all data by the label studio. We can go back to our notebook to train the model.
Open a terminal.
After executed, it will create a folder named data and place the labeled images into the correct folder inside data folder.
We successfully use our labeled data to train a model which can classify whether the screw is good or bad!
Open the notebook app_tutorial_labelstudio_screw_train.ipynb and execute all cells. In the last cell, you will see the result which is similar to the following image.
