Deepen AI - Enterprise
Deepen AI
  • Deepen AI Overview
  • FAQ
  • Saas & On Premise
  • Data Management
    • Data Management Overview
    • Creating/Uploading a dataset
    • Create a dataset profile
    • Auto Task Assignments
    • Task Life Cycle
    • Tasks Assignments
    • Creating a group
    • Adding a user
    • Embed labeled dataset
    • Export
    • Export labels
    • Import Labels
    • Import profile
    • Import profile via JSON
    • Access token for APIs
    • Data Streaming
    • Reports
    • Assessments
  • 2D/3D editors
    • Editor Content
    • AI sense (ML-assisted labeling)
    • Assisted 2d Segmentation
    • Scene Labeling
  • 2D Editor
    • 2D Editor Overview
    • 2D Bounding Boxes
    • 2D Polyline/Line
    • 2D Polygon
      • 2D Semantic/Instance Segmentation
        • 2D Segmentation (foreground/background)
    • 2D Points
    • 2D Semantic Painting
      • Segment Anything
      • Propagate Labels in Semantic Segementation
      • 2D Semantic Painting/Segmentation Output Format
    • 3D Bounding boxes on images
    • 2D ML-powered Visual Object Tracking
    • 2D Shortcut Keys
    • 2D Customer Review
  • 3D Editor
    • 3D Editor Overview
    • 3D Bounding Boxes — Single Frame/Individual Frame
    • 3D Bounding Boxes_Sequence
    • 3D Bounding Boxes Features
      • Label View
      • One-Click Bounding Box
      • Sequence Timeline
      • Show Ground Mesh
      • Secondary Views
      • Camera Views
      • Hide/UnHide Points in 3D Lidar
    • 3D Lines
    • 3D Polygons
    • 3D Semantic Segmentation/Painting
    • 3D Instance Segmentation/Painting
    • Fused Cloud
    • 3D Segmentation (Smart Brush)
    • 3D Segmentation (Polygon)
    • 3D Segmentation (Brush)
    • 3D Segmentation (Ground Polygon)
    • 3D Painting (Foreground/Background)
    • 3D Segmentation(3D Brush/Cube)
    • Label Set
    • 3D Shortcut Keys
    • 3D Customer Review
  • 3D input/output
    • JSON input format for uploading a dataset in a point cloud project.
    • How to convert ROS bag into JSON data for annotation
    • Data Output Format - 3D Semantic Segmentation
    • Data Output Format - 3D Instance Segmentation
  • Quality Assurance
    • Issue Creation
    • Automatic QA
  • Calibration
    • Calibration
    • Charuco Dictionary
    • Calibration FAQ
    • Data Collection for Camera intrinsic Calibration
    • Camera Intrinsic calibration
    • Data Collection for Lidar-Camera Calibration (Single Target)
    • Lidar-Camera Calibration (Single target)
    • Data Collection for Lidar-Camera Calibration (Targetless)
    • Lidar-Camera Calibration (Targetless)
    • Data Collection for Multi Target Lidar-Camera Calibration
    • Multi Target Lidar-Camera Calibration
    • Lidar-Camera Calibration(Old)
    • Vehicle-Camera Calibration
      • Data Collection for Vehicle-Camera Calibration
      • Vehicle Camera Targetless Calibration
      • Data collection for lane based targetless vehicle-camera calibration
      • Lane based Targetless Vehicle Camera Calibration
    • Data Collection for Rough Terrain Vehicle-Camera Calibration
    • Rough Terrain Vehicle-Camera Calibration
    • Calibration Toolbar options
    • Calibration Profile
    • Data Collection for Overlapping-Camera Calibration
    • Overlapping-Camera Calibration
    • Data collection guide for Overlapping Camera Calibration (Multiple-Targets)
    • Overlapping Camera Calibration (Multiple-Targets)
    • Data Collection for Vehicle-3D Lidar calibration
    • Data Collection for Vehicle-2D Lidar calibration
    • Vehicle Lidar (3D and 2D) Calibration
    • Data Collection for Vehicle Lidar Targetless Calibration
    • Data Collection for IMU Lidar Targetless Calibration
    • Vehicle Lidar Targetless Calibration
    • Data Collection for Non Overlapping Camera Calibration
    • Non-Overlapping-Camera Calibration
    • Multi Sensor Visualization
    • Data Collection for LiDAR-LiDAR Calibration
    • LiDAR-LiDAR Calibration
    • Data Collection for IMU Intrinsic calibration
    • IMU Intrinsic Calibration
    • Data Collection for Radar-Camera Calibration
    • Radar-Camera Calibration
    • Data Collection for IMU Vehicle calibration
    • Lidar-IMU Calibration
    • IMU Vehicle Calibration
    • Data Collection for vehicle radar calibration
    • Vehicle radar calibration
    • Calibration Optimiser
    • Calibration list page
    • Data collection for rough terrain vehicle-Lidar calibration
    • Rough terrain vehicle Lidar calibration
    • Surround view camera correction calibration
    • Data Collection for Surround view camera correction calibration
    • Data Collection for Lidar-Radar calibration
    • Lidar Radar Calibration
    • Vehicle Lidar Calibration
    • API Documentation
      • Targetless Overlapping Camera Calibration API
      • Target Overlapping Camera Calibration API
      • Lidar Camera Calibration API
      • LiDAR-LiDAR Calibration API
      • Vehicle Lidar Calibration API
      • Global Optimiser
      • Radar Camera Calibration API
      • Target Camera-Vehicle Calibration API
      • Targetless Camera-Vehicle Calibration API
      • Calibration groups
      • Delete Calibrations
      • Access token for APIs
    • Target Generator
  • API Reference
    • Introduction and Quickstart
    • Datasets
      • Create new dataset
      • Delete dataset
    • Issues
    • Tasks
    • Process uploaded data
    • Import 2D labels for a dataset
    • Import 3D labels for a dataset
    • Download labels
    • Labeling profiles
    • Paint labels
    • User groups
    • User / User Group Scopes
    • Download datasets
    • Label sets
    • Resources
    • 2D box pre-labeling model API
    • 3D box pre-labeling model API
    • Output JSON format
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  1. Quality Assurance

Automatic QA

PreviousIssue CreationNextCalibration

Last updated 3 years ago

Auto-Checker:

We get a list of suggestions when we click the Auto-check button available in the Issues tab. If there are any issues, the user gets a list of issues.

From the suggestion list, for each suggestion, we can either create an Issue by clicking the Accept button or reject the issue by clicking the Reject button.

We can create Issues from the rejected suggestions as well.

Configure Auto-checker Tab

Based on the configuration given here, we can control Auto checker suggestions List Item.

  1. From the options in the first section, we can see either Active suggestions or Rejected suggestions

2. We have the following filters for the Auto checker suggestions.

  1. Issue Type

  2. Label Type

  3. Issue Title

Other Important Issue Flow Features

Visual Sync between Editor and Issues Panel

To see the visible sync for the same issue displayed in the Editor and Issues Panel,

  1. Select Create Issue primary toolbar mode.

  2. Open the Issue panel and select the Issues tab to see the issues list.