Frequently Asked Questions

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.

A dataset profile in Deepen AI is a reusable configuration template that defines categories, attributes, labeling instructions, and workflow settings for datasets. Creating a profile streamlines the annotation process for projects involving images, videos, or 3D point clouds, ensuring consistency and efficiency across datasets, especially for autonomous vehicle and robotics applications.

To create a dataset profile, navigate to the left panel in your Deepen AI workspace and click on “Dataset Profile.” Then, enter a profile name (e.g., “My Image Profile”) using alphanumeric characters to begin configuring the profile settings.

Categories represent object types to be annotated, such as “car,” “pedestrian,” or “traffic sign.” To configure a category, select a label type (e.g., bounding box, segmentation), type the category name, and press Enter in the provided box. This setup ensures precise labeling tailored to your project’s needs, such as autonomous driving or robotic perception.

Yes, for 3D bounding boxes, you can configure the default bounding box size by clicking on the specific category in the dataset profile settings. This is particularly useful for LiDAR-based 3D point cloud datasets, ensuring accurate annotations for objects like vehicles or obstacles.

Label attributes are specific properties assigned to a category, such as “color” or “occlusion” for a “car” category. After selecting a category in the dataset profile, you can add these attributes to customize annotations, enhancing the granularity of data for machine learning models.

To enable auto-task assignments, check the “Enable auto-task assignments” option during dataset profile creation. This feature automates task distribution to annotators, reducing manual effort and speeding up the annotation process for large-scale projects.

Labeling instructions are optional but recommended. They provide annotators with clear guidelines to ensure consistent and accurate labeling, which is critical for generating high-quality ground truth data for AI models in safety-critical applications like autonomous vehicles.

To configure the workflow pipeline, input a name for each pipeline stage and check the box to allow labeling activity for that stage. You can add multiple stages to create a structured annotation process, ensuring quality control and efficient task management tailored to your project’s requirements.

In the Deepen AI Image Editor, you can assign attributes to labeled objects using shortcut keys or directly through the editor’s interface. This feature allows annotators to efficiently add properties like “size” or “type” to objects, enhancing the usability of the platform for 2D image annotations.

A dataset profile standardizes annotation settings across images, videos, or 3D point clouds, supporting Deepen AI’s multi-sensor calibration and labeling capabilities. This ensures consistency for complex datasets involving cameras, LiDAR, or radar, critical for autonomous systems and robotics.

Yes, once created, a dataset profile can be imported into new datasets, saving time by reusing predefined categories, attributes, and workflow settings. This is particularly useful for teams managing multiple projects with similar annotation requirements.

Deepen AI’s platform is designed for safety-first data lifecycle management, offering customizable profiles with precise category and attribute configurations. Features like workflow pipelines and auto-task assignments ensure high-quality, consistent annotations for machine learning models used in autonomous vehicles and robotics.

Visit the Deepen AI website at https://www.deepen.ai/ or explore their tools page at https://tools.deepen.ai/ for detailed guidance. You can also contact their support team at info@deepen.ai for assistance with dataset profile creation or other platform features.