Label Dataset by Label Studio
This tutorial covers the basic flow to help you get started with Label Studio in PrimeHub.
Install Label Studio
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.
Label Studio UI
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.
Label Dataset
What we need?
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.
Steps
After login, please click
Create
button.Enter your
Project Name
. Skip theData Import
step. And choose theLabeling Setup
. Here we chooseSemantic Segmentation with Polygons
.Delete the original
Labels
settings andAdd
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 setLocal 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!
How to Use Labeled Data to Train a Model
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.
What we need?
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 notebookCreate a directory
/project/<group_name>/screw-labeled
in group volume to save the labeled resultsThe 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 notebookThe 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.
Steps
Follow the previous
Label Dataset
section to use the label studio. This time inLabeling Setup
, we should chooseImage Classification
.Delete the original
Labels
settings andAdd
our own label classes:bad
,good
.Click the
Settings
on the upper-right. ClickCloud Storage
andAdd Source Storage
to sync the/datasets/screw
data volume to label. SetLocal path
to/datasets/screw
, setFile Filter Regex
to.*png
, turn on toggle ofTreat every bucket object as a source file
. After added, clickSync Storage
.Click
Add Target Storage
to sync to labeled results to/project/<group_name>/screw-labeled
. You need to setLocal 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 insidedata
folder.
We successfully use our labeled data to train a model which can classify whether the screw is good or bad!
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