Python code annotation12/5/2023 ![]() In this example, import predictions for an image classification task: project.import_tasks( One way is to import tasks in a simple JSON format, where one key in the JSON identifies the data object being labeled, and the other is the key containing the prediction. The SDK offers several ways that you can import pre-annotations into Label Studio. You can also import predictions together with tasks as pre-annotated tasks. Import pre-annotated tasks into Label Studio Project.create_prediction(task_ids, result= 'Dog', score= 0.9)įor complex cases, such as object detection with bounding boxes, you can specify structured results: project.create_prediction(task_ids, result=, score= 0.9)įor another example, see the Jupyter notebook example of importing pre-annotated data. You can add predictions to existing tasks in Label Studio in your Python script.įor an existing simple image classification project, you can do the following to add predictions of “Dog” for image tasks that you retrieve: task_ids = project.get_tasks_ids() project.import_tasks(Īdd predictions to existing tasks with the Label Studio Python SDK You can import tasks from your script using the Label Studio Python SDK.įor a specific project, you can import tasks in Label Studio JSON format or connect to cloud storage providers and import image, audio, or video files directly. Import tasks with the Label Studio Python SDK įor example, create an audio transcription project in your Python code: project = ls.start_project(įor more about what you can do with the project module of the SDK, see the project module SDK reference. See the available templates for Label Studio projects, or set a blank configuration with. Choose your labeling configuration based on the type of labeling that you wish to perform. Specify the project title and the labeling configuration. Ls = Client(url=LABEL_STUDIO_URL, api_key=API_KEY)Ĭreate a project with the Label Studio Python SDKĬreate a project in Label Studio using the SDK. # Connect to the Label Studio API and check the connection LABEL_STUDIO_URL = ' API_KEY = 'd6f8a2622d39e9d89ff0dfef1a80ad877f4ee9e3' # Import the SDK and the client module from label_studio_sdk import Client # Define the URL where Label Studio is accessible and the API key for your user account Define your API key and Label Studio URL (API key is available at Account page).In your Python script, do the following:.See the full SDK reference documentation for all available modules, or review the available API endpoints for any tasks that the SDK does not cover. Modify project settings, such as task sampling or the model version used to display predictions.Connect to a cloud storage provider, such as Amazon S3, Microsoft Azure, or Google Cloud Services (GCS), to retrieve unlabeled tasks and store annotated tasks.Manage pre-annotated tasks and model predictions. ![]() Create a Label Studio project, including setting up a labeling configuration.With the Label Studio Python SDK, you can perform the following tasks in a Python script: This software development kit (SDK) lets you call the Label Studio API directly from scripts using predefined classes and methods. You can use the Label Studio Python SDK to make annotating data a more integrated part of your data science and machine learning pipelines.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |