Who Uses Avala
- Autonomous Vehicle Teams — Label camera images, LiDAR point clouds, and synchronized multi-sensor recordings for perception model training.
- Robotics Companies — Annotate perception data for navigation, manipulation, and scene understanding.
- AI/ML Teams — Create training datasets for object detection, segmentation, classification, and tracking.
- Research Labs — Build labeled datasets for computer vision and 3D perception research.
Platform
Mission Control
Web-based annotation interface for labeling, project management, and quality control.
REST API
Programmatic access to datasets, projects, tasks, and exports at
server.avala.ai/api/v1.SDKs
Official Python (
avala) and TypeScript (@avala/sdk) clients with full type support.Integrations
MCP server for AI assistants, S3-compatible cloud storage, MCAP/ROS pipelines, and webhooks.
Supported Data Types
Avala handles five data modalities, each with purpose-built annotation workflows:| Data Type | Formats | Description |
|---|---|---|
| Images | JPEG, PNG, WebP | Single-frame annotation with all 2D tools |
| Video | MP4, MOV | Converted to frame sequences for frame-by-frame annotation and object tracking |
| Point Clouds | PCD, PLY | 3D LiDAR scans with cuboid annotation and bird’s-eye view |
| MCAP / ROS | MCAP | Multi-sensor container with camera, LiDAR, and IMU data; multi-camera projection |
| Splat | Gaussian Splat | 3D scene annotation in Gaussian Splat environments |
Annotation Tools
Professional annotation tools for every use case:- Bounding Boxes — 2D rectangular regions for object detection
- Polygons — Arbitrary shapes for precise object boundaries
- 3D Cuboids — 3D bounding boxes in point cloud and multi-sensor data
- Segmentation — Pixel-level classification masks
- Polylines — Path, lane, and edge annotations
- Keypoints — Landmark and pose annotations
- Classification — Scene-level and object-level attribute labels