# Import Labels ## Overview The Import Labels Plugin allows you to import pre-labels into a project from a CSV file. This plugin is designed to support bulk ingestion of labels generated by external tools, models, or annotation pipelines and map them correctly to existing assets in your project.
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Import Labelshttps://angohub-docs-assets.s3.eu-central-1.amazonaws.com/plugin-icons-banners/csv-import-labels.png
## Plugin Functionality The plugin reads a CSV file where each row represents a single asset and imports labels based on the configured parameters. Depending on the configuration, it can: * Match CSV rows to existing project assets using an external ID column * Import classification labels from one or more CSV columns * Import segmentation/polygon annotations from mask files referenced in the CSV * Map mask values to label classes using project colors or custom mappings * Overwrite existing labels or append new ones * Process imports in customized batches for better performance.
#### Example Configurations **Import Classification Annotations** {% code title="Config JSON" fullWidth="false" %} ```json { "external_id_column": "External ID", "classification_columns": ["Color", "Category", "Image Type"], "classification_schema_ids": ["8c481a58f4c68e5db0a5016", "12a20f8061c044bcff26688", "1976d3fc0fa1429e23f7171"], "mask_column": "", "mask_color_mapping": "project_colors", "segmentation_mode": "instance_segmentation", "polygon_simplification_tolerance": 0.1, "storage_id": "", "overwrite": true, "ignored_schema_ids": [], "upload_batch_size": 100 } ``` {% endcode %} {% file src="/files/ep3tF5A1PctAALEmP7Oj" %}
Category Schema ```json { "tools": [], "classifications": [ { "title": "Color", "tool": "text", "required": false, "schemaId": "8c481a58f4c68e5db0a5016", "frameSpecific": false, "richText": false, "regex": "", "classifications": [], "multiple": false, "options": [], "shortcutKey": "1" }, { "title": "Type", "tool": "radio", "required": false, "schemaId": "12a20f8061c044bcff26688", "frameSpecific": false, "classifications": [], "multiple": false, "options": [ { "value": "A", "schemaId": "0ab9ff484461bb8d5952369" }, { "value": "B", "schemaId": "23b5e8770c797a9ad036777" }, { "value": "C", "schemaId": "64989cbf9f9ddd38b882118" } ], "shortcutKey": "2" }, { "title": "Question", "tool": "checkbox", "required": false, "schemaId": "1976d3fc0fa1429e23f7171", "frameSpecific": false, "classifications": [], "multiple": false, "options": [ { "value": "Option1", "schemaId": "1c00ef1ec8bca05c3550770" }, { "value": "Option2", "schemaId": "b90770265328965f1f7c070" } ], "shortcutKey": "3" } ], "relations": [] } ```
*** **Import Segmentation Annotations** {% code title="Config JSON" fullWidth="false" %} ```json { "external_id_column": "External ID", "classification_columns": ["Color", "Category", "Image Type"], "classification_schema_ids": ["8c481a58f4c68e5db0a5016", "12a20f8061c044bcff26688", "1976d3fc0fa1429e23f7171"], "mask_column": "Mask", "mask_color_mapping": "project_colors", "segmentation_mode": "instance_segmentation", "polygon_simplification_tolerance": 0.1, "storage_id": "", "overwrite": true, "ignored_schema_ids": [], "upload_batch_size": 100 } ``` {% endcode %} {% file src="/files/x07sjsvd92c1kfdzldxn" %}
Category Schema ```json { "tools": [ { "title": "Car", "tool": "segmentation", "required": false, "schemaId": "f104b6b9cdb84966812b002", "classifications": [], "multiple": false, "color": "#f44336", "shortcutKey": "4" }, { "title": "Person", "tool": "segmentation", "required": false, "schemaId": "c6faa323be0cbb84634f566", "classifications": [], "multiple": false, "color": "#03a9f4", "shortcutKey": "5" }, { "title": "Other", "tool": "segmentation", "required": false, "schemaId": "b34867a913e35379d100997", "classifications": [], "multiple": false, "color": "#9c27b0", "shortcutKey": "6" } ], "classifications": [ { "title": "Color", "tool": "text", "required": false, "schemaId": "8c481a58f4c68e5db0a5016", "frameSpecific": false, "richText": false, "regex": "", "classifications": [], "multiple": false, "options": [], "shortcutKey": "1" }, { "title": "Type", "tool": "radio", "required": false, "schemaId": "12a20f8061c044bcff26688", "frameSpecific": false, "classifications": [], "multiple": false, "options": [ { "value": "A", "schemaId": "0ab9ff484461bb8d5952369" }, { "value": "B", "schemaId": "23b5e8770c797a9ad036777" }, { "value": "C", "schemaId": "64989cbf9f9ddd38b882118" } ], "shortcutKey": "2" }, { "title": "Question", "tool": "checkbox", "required": false, "schemaId": "1976d3fc0fa1429e23f7171", "frameSpecific": false, "classifications": [], "multiple": false, "options": [ { "value": "Option1", "schemaId": "1c00ef1ec8bca05c3550770" }, { "value": "Option2", "schemaId": "b90770265328965f1f7c070" } ], "shortcutKey": "3" } ], "relations": [] } ```
### Supported Data Types * Compatible with all data types available in AngoHub. ### Supported Annotation Tools * Classifications * Radio * Checkbox * Single-Select Dropdown * Multi-Select Dropdown * Text * Tools * Segmentation * Polygon {% hint style="danger" %} Following tools are not supported by the plugin: * Nested classifications, multiple classifications, frame specific classifications * Frame-specific segmentation and polygon tools * Nested classifications within tools {% endhint %} ## Plugin Configuration You may vary a number of settings related to your export from the **Config JSON** field. Each option is detailed below: ```json { "external_id_column": "", "classification_columns": [], "classification_schema_ids": [], "mask_column": "", "mask_color_mapping": "project_colors", "segmentation_mode": "instance_segmentation", "polygon_simplification_tolerance": 0.1, "storage_id": "", "overwrite": true, "ignored_schema_ids": [], "upload_batch_size": 100 } ``` * **"external\_id\_column":** Name of the CSV column that contains external IDs used to match imported labels to existing assets in the project. * Example: * "external\_id\_column": "External ID" * **"classification\_columns":** List of CSV column names that contain classification values to import. Each column is mapped to a classification schema. * Example: * "classification\_columns": \["Color", "Category"] * **"classification\_schema\_ids":** List of classification schema IDs that define how values from classification\_columns are interpreted and mapped during import. The order must match classification\_columns. * Example: * "classification\_schema\_ids": \["12345", "12346"] * **"mask\_column":** Name of the CSV column that contains segmentation mask references. The value can be a file path or URL pointing to the mask file. * Example: * "mask\_column": "Mask" * **"mask\_color\_mapping":** Defines how colors in segmentation masks are mapped to label classes. Use project colors or provide a custom mapping. * Options: * "mask\_color\_mapping": "project\_colors" * "mask\_color\_mapping": {"Car" : \[244, 67, 54], "Person" : \[3, 169, 244]} * "mask\_color\_mapping": {"Car" : \[1], "Person" : \[2]} * **"segmentation\_mode":** Controls how segmentation annotations are created from masks. "instance\_segmentation" creates a separate object for each connected region per class and "semantic\_segmentation" creates a single object for each class. * Options: * "segmentation\_mode": "instance\_segmentation" * "segmentation\_mode": "semantic\_segmentation" * **"polygon\_simplification\_tolerance":** Tolerance used when converting masks to polygons. Higher values reduce the number of polygon points at the cost of geometric precision. * Example: * "polygon\_simplification\_tolerance": 0.1 * **"storage\_id":** Identifier of the storage location where mask files referenced in the CSV are stored. Used to resolve file paths or storage keys during import. * Example: * "storage\_id": "1234" * **"overwrite":** If true, existing labels for matching assets will be replaced by the imported labels. If false, imported labels are added while preserving existing labels. * Example: * "overwrite": true * "overwrite": false * **"ignored\_schema\_ids":** List of schema IDs to ignore during import. Any tools or classifications associated with these schemas will be skipped, even if present in the CSV. * Example: * "ignored\_schema\_ids": \["1234", "1235"] * **"upload\_batch\_size":** Number of rows from the CSV to process per batch. Larger batch sizes improve throughput but increase memory usage and may hit API limits. * Example: * "upload\_batch\_size": 100 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.imerit.net/plugins/first-party-ango-plugins/import-labels.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.