This page covers the building blocks of the Avala platform: datasets, projects, tasks, organizations, labels, annotation types, quality control, and sequences. Understanding these concepts will help you design effective annotation workflows.
Datasets
A dataset is a collection of data items (images, video frames, point clouds, or multi-sensor recordings) that serve as the raw material for annotation.
Dataset Properties
| Property | Description |
|---|
name | Human-readable name |
slug | URL-friendly identifier (unique within the owner’s namespace) |
data_type | Type of data: image, video, lidar, mcap, image_3d, splat |
visibility | public or private |
owner | User or organization that owns the dataset |
item_count | Total number of data items in the dataset |
Data Items
Each dataset contains items — individual data samples:
- Image datasets — Each item is a single image file.
- Video datasets — Items are video frames, grouped into sequences.
- LiDAR datasets — Items are individual point cloud scans.
- MCAP datasets — Items contain synchronized multi-sensor frames (camera + LiDAR + IMU).
Sequences
Sequences group related items for temporal or multi-frame data:
- Video frames from the same recording
- LiDAR scans from a continuous driving session
- Synchronized multi-camera captures at consecutive timestamps
Sequences enable frame-by-frame navigation, object tracking across frames, and temporal consistency in annotations.
Sequence status workflow:
uploading → processing → ready → failed
Projects
A project defines an annotation workflow by connecting one or more datasets to a specific task type, label taxonomy, and quality control configuration.
Project Components
Project
├── Datasets (data sources)
├── Task Type (annotation method)
├── Label Config (object classes, attributes)
├── Quality Control (review stages, consensus)
└── Tasks (individual work units)
Task Types
Projects are configured with one of the following task types:
| Task Type | API Value | Description |
|---|
| Image Annotation | image-annotation | 2D annotation on single images (boxes, polygons, segmentation, keypoints) |
| Video Annotation | video-annotation | Frame-by-frame annotation with object tracking across frames |
| Point Cloud Annotation | point-cloud-annotation | 3D annotation on LiDAR scans (cuboids, segmentation) |
| Point Cloud Objects | point-cloud-objects | Object-level annotation in 3D point cloud sequences |
Project Status
| Status | Description |
|---|
draft | Being configured, not yet generating tasks |
active | Accepting annotation work |
paused | Temporarily halted |
cancelled | Permanently stopped |
archived | Completed and archived |
Tasks
A task is an individual work unit within a project. Each task represents annotation work to be done on one or more data items by a single annotator.
Task Lifecycle
Tasks progress through the following states:
pending → assigned → in_progress → submitted → under_review → approved
→ rejected → rework
| Status | Description |
|---|
pending | Created but not yet assigned to an annotator |
assigned | Assigned to an annotator, waiting for them to start |
in_progress | Annotator is actively working on the task |
submitted | Annotator has submitted their work for review |
under_review | A reviewer is examining the submitted annotations |
approved | Annotations accepted — task is complete |
rejected | Annotations did not pass review |
rework | Returned to the annotator for corrections |
Results
When an annotator completes a task, they submit a result containing:
- The annotation data (bounding boxes, polygons, cuboids, segmentation masks, etc.)
- Metadata (time spent, tool versions)
Results go through quality control review before final acceptance.
Organizations
An organization groups users and resources for team-based collaboration.
Organization Structure
Organization
├── Members (users with roles)
├── Datasets (shared data)
├── Projects (shared workflows)
└── Settings (billing, API keys, permissions)
Member Roles
| Role | Capabilities |
|---|
owner | Full control — billing, settings, can delete the organization |
admin | Manage members, create and configure resources |
member | Access shared resources, perform annotation work |
Labels and Taxonomy
Label Config
Projects define a label config — a set of predefined object classes that annotators assign to annotations:
{
"labels": [
{ "name": "car", "color": "#FF0000" },
{ "name": "pedestrian", "color": "#00FF00" },
{ "name": "cyclist", "color": "#0000FF" }
]
}
Classification
For more complex taxonomies, projects can include classification configs that define:
- Attributes — Properties like color, occlusion level, or truncation that annotators assign to each object.
- Hierarchical categories — Nested class structures (e.g., Vehicle > Car > Sedan).
- Conditional attributes — Attributes that only appear for specific object classes.
Annotation Types
Avala supports the following annotation types, each designed for specific labeling tasks:
| Type | Description | Data Types |
|---|
| Bounding Box | 2D rectangular region around an object | Images, Video |
| Polygon | Arbitrary closed shape tracing object boundaries | Images, Video |
| 3D Cuboid | 3D bounding box with position, dimensions, and rotation | Point Clouds, MCAP |
| Segmentation | Pixel-level classification mask | Images, Video |
| Polyline | Open path for lanes, edges, and boundaries | Images, Video |
| Keypoints | Landmark points for pose estimation and structure | Images, Video |
| Classification | Scene-level or object-level categorical labels | All data types |
Quality Control
Avala provides built-in quality assurance tools to ensure annotation accuracy and consistency.
Reviews
Annotations go through a review stage before acceptance:
- Annotator submits their result.
- A reviewer examines the annotations.
- The reviewer approves correct work or rejects work that needs correction.
- Rejected tasks return to the annotator for rework.
Issues
Annotation issues let reviewers flag specific problems on individual annotations:
- Pin an issue to a specific object or region in the scene.
- Assign issues to team members for resolution.
- Track issue status (open, resolved).
Metrics
Monitor annotation quality with built-in metrics:
- Acceptance rate — Percentage of tasks approved on first submission.
- Annotation time — Average time spent per task.
- Inter-annotator agreement — Consistency across annotators on the same data.
- Issue frequency — Rate of flagged problems per task.
Consensus
Consensus workflows assign the same data to multiple annotators independently, then compare results to measure agreement and identify ambiguous cases.
Quality metrics help identify training needs and maintain consistent annotation standards across your team.
Sequences
Sequences are ordered collections of data items that represent temporal or spatial progressions — video frames, LiDAR sweeps, or multi-sensor recordings.
Properties
| Property | Description |
|---|
name | Sequence identifier |
frame_count | Number of frames in the sequence |
status | Processing status of the sequence |
data_type | Inherited from the parent dataset |
Status Workflow
Sequences follow this status progression as data is uploaded and processed:
uploading → processing → ready
→ failed
- uploading — Frames are being uploaded to the platform.
- processing — Frames are being validated and prepared for annotation.
- ready — All frames are processed and available for annotation.
- failed — Processing encountered an error (check individual frame statuses).
Next Steps
