Recently I have been trying to do model training work related to image target detection, but if you want to achieve the training of a model, the data set is crucial, and the production of the target detection data set is more complicated than image classification, so a simple and easy-to-use data set is needed. After comparing a bunch of tools on the Internet, I finally chose label studio, mainly for the following reasons:
- Data security: It is expected that the labeled data can only be used internally and does not want to be exposed to the outside world.
- Convenience: Annotation can be implemented by deploying a web service, so that other students can also annotate together later.
- Automatic/semi-automatic annotation: The trained model can be combined to assist annotation to achieve automatic/semi-automatic annotation, and the target detection effect of the trained model can also be visualized and verified. Finally, it was found that label studio meets the above requirements, so the Label Studio labeling tool was built, and combined with the YOLO model to achieve automatic/semi-automatic labeling of the data set, the entire process is now recorded.
Environment setup
Basic environment setup
css Copy code > conda create -n label-studio python=3.10 > conda activate label-studio
Front-end service
shell Copy code > cd /data/code/github/label-studio # Run database migrations > python label_studio/manage.py migrate > python label_studio/manage.py collectstatic # Start the server in development mode at http://localhost:8080 python label_studio/manage.py runserver > python label_studio/server.py --host=0.0.0.0 --port=8081
The data saving path is as follows:
javascript Copy code ~/.local/share/label-studio/
As follows
yaml Copy code (label-studio) # ls -lrt ~/.local/share/label-studio/ total 1212 drwxr-xr-x 2 xxxx xxxx 4096 Oct 20 10:36 test_data drwxr-xr-x 4 xxxx xxxx 4096 Oct 20 11:02 media drwxr-xr-x 2 xxxx xxxx 4096 Oct 20 12:05 export -rw-r--r-- 1 xxxx xxxx 1228800 Oct 20 15:30 label_studio.sqlite3
- Project interface
- Create a new project
- basic settings
The type of labeled objects can be set in Labeling Interface.
- Backend inference
Machine Learning can set up a back-end inference service to achieve automated annotation. You can refer to label-studio-ml-backend. If Yolov8 is used for detection, you can refer to label-studio-yolov8-backend. If combined with SAM for detection, Can refer to Split Everything? Teach you step by step how to deploy SAM + LabelStudio to achieve automatic labeling
Retrieve predictions when loading a task automatically: The imported image will be automatically called to the prediction service
- Final effect
The numbers below are the shortcut keys for the corresponding types
Annotation results can be cleaned
- Delete Tasks: Delete pictures and tasks
- Delete Annotations: Delete manual annotation results
- Delete Predictions: To delete the prediction results, you need to connect to the back-end prediction service. If you are not satisfied with the prediction results and want to re-predict, you can delete the prediction results and regenerate the prediction results.
The annotated data code is as follows:
json
Copy code
{
"id": 50,
"data": {
"image": "/data/upload/7/437ac54a-csm_eb24002_58f4fb61dd.jpg"
},
"annotations": [
{
"id": 5,
"created_username": " [email protected], 2",
"created_ago": "0 minutes",
"completed_by": {
"id": 2,
"first_name": "",
"last_name": "",
"avatar": null,
"email": "[email protected]",
"initials": "li"
},
"result": [
{
"original_width": 1366,
"original_height": 768,
"image_rotation": 0,
"value": {
"x": 10.10689990281827,
"y": 27.483448736637506,
"width": 23.420796890184647,
"height": 37.68170958859735,
"rotation": 0,
"rectanglelabels": [
"Car"
]
},
"id": "8oMV4M5avz",
"from_name": "label",
"to_name": "image",
"type": "rectanglelabels",
"origin": "manual"
}
],
"was_cancelled": false,
"ground_truth": false,
"created_at": "2023-10-22T09:21:27.660411Z",
"updated_at": "2023-10-22T09:21:27.660435Z",
"draft_created_at": "2023-10-22T09:19:22.672990Z",
"lead_time": 114.60199999999999,
"import_id": null,
"last_action": null,
"task": 50,
"project": 7,
"updated_by": 2,
"parent_prediction": null,
"parent_annotation": null,
"last_created_by": null
}
],
"predictions": []
}
Backend prediction service
Backend prediction services can be deployed to implement automatic annotation
shell Copy code > git clone https://github.com/HumanSignal/label-studio-ml-backend.git > cd label-studio-ui-element-backend > pip install -e . # Environment verification > label-studio-ml create env_verify > label-studio-ml start env_verify -p 9091
- Create a formal environment
css Copy code > label-studio-ml create ui_element > label-studio-ml start ui_element -p 9091 > nohup label-studio-ml start ui_element -p 9091 > ./ui_element/label.log 2> & amp;1 & amp; > nohup label-studio-ml start ui_element_sam -p 9092 > ./ui_element_sam/label.log 2> & amp;1 & amp;
Export annotation results
All annotation results will be exported, and different annotation formats can be exported. For example, if used for YOLO training, the YOLO format can generally be used. Of course, there are also COCO and VOCdata format
Currently, there is no way to export only the annotation results of the selected images. For example, the exported YOLO data format is as follows:
-
images: Annotated pictures
-
labels: label data, saved with txt suffix, each line represents a piece of label data, containing 5 columns
- Column 1: The marked category is consistent with the category index in
classex.txtbelow - Column 2: x-coordinate of the center point of the marked rectangular frame (normalized: coordinate point/picture width)
- Column 3: The y coordinate of the center point of the marked rectangular frame (normalized: coordinate point/picture height)
- Column 4: Width of the labeled rectangular box (normalized: width/picture width)
- Column 5: Height of the labeled rectangular box (normalized: height/picture height)
- Column 1: The marked category is consistent with the category index in
Copy code 0 0.7914317925591883 0.3476714942329373 0.3066516347237879 0.1781053970456451 0 0.7931228861330326 0.5379758910762293 0.30552423900789166 0.16834619720752755 0 0.7931228861330328 0.7221807880206979 0.30777903043968435 0.1756655970861157
- classes.txt: Save the annotated type list
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