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---
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title: 'Automatic annotation'
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linkTitle: 'Automatic annotation'
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weight: 16
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description: 'Guide to using the automatic annotation of tasks.'
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---
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Automatic Annotation is used for creating preliminary annotations.
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To use Automatic Annotation you need a DL model that can be deployed by a CVAT administrator.
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You can find the list of available models in the `Models` section.
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1. To launch automatic annotation, you should open the dashboard and find a task which you want to annotate.
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Then click the `Actions` button and choose option `Automatic Annotation` from the dropdown menu.
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1. In the dialog window select a model you need. DL models are created for specific labels, e.g.
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the Crossroad model was taught using footage from cameras located above the highway and it is best to
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use this model for the tasks with similar camera angles.
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If it's necessary select the `Clean old annotations` checkbox.
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Adjust the labels so that the task labels will correspond to the labels of the DL model.
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For example, let’s consider a task where you have to annotate labels “car” and “person”.
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You should connect the “person” label from the model to the “person” label in the task.
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As for the “car” label, you should choose the most fitting label available in the model - the “vehicle” label.
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If the chosen model supports automatic attributes detection
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(like facial expressions, for example: ``serverless/openvino/omz/intel/face-detection-0205``),
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you can also map attributes between the DL model and your task.
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The task requires to annotate cars only and choosing the “vehicle” label implies annotation of all vehicles,
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in this case using auto annotation will help you complete the task faster.
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Click `Submit` to begin the automatic annotation process.
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1. At runtime - you can see the percentage of completion.
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You can cancel the automatic annotation by clicking on the `Cancel`button.
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1. The end result of an automatic annotation is an annotation with separate rectangles (or other shapes)
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1. You can combine separate bounding boxes into tracks using the `Person reidentification ` model.
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To do this, click on the automatic annotation item in the action menu again and select the model
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of the `ReID` type (in this case the `Person reidentification` model).
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You can set the following parameters:
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- Model `Threshold` is a maximum cosine distance between objects’ embeddings.
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- `Maximum distance` defines a maximum radius that an object can diverge between adjacent frames.
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1. You can remove false positives and edit tracks using `Split` and `Merge` functions.
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