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This guide covers how to migrate your annotation data and workflows from other platforms to Avala. Whether you’re coming from CVAT, Labelbox, Label Studio, or another tool, Avala supports importing data in common annotation formats.

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Overview

Migrating to Avala generally follows three steps:
  1. Export your data and annotations from your current platform
  2. Transform the exported data into an Avala-compatible format if needed
  3. Import the data into Avala via the API, SDK, or Mission Control
Need help with a large or complex migration? Contact us at support@avala.ai for assisted migration support. Our team can help with custom format conversions, bulk imports, and data validation.

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Migrating from CVAT

CVAT supports exporting annotations in several formats that Avala can import directly.

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Step 1: Export from CVAT

  1. Open your CVAT project or task
  2. Click Export task dataset (or Export project dataset)
  3. Select one of these formats:
    • COCO 1.0 (recommended for image annotations)
    • CVAT for images 1.1 (XML format)
    • YOLO 1.1 (for bounding box annotations)
  4. Download the exported archive

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Step 2: Import to Avala

Upload your images to an Avala dataset, then import the annotations: 
import avala
import os

client = avala.Client(api_key=os.environ["AVALA_API_KEY"])

# Create a dataset and upload images
dataset = client.datasets.create(name="migrated-from-cvat", data_type="image")

# Import COCO annotations
client.datasets.import_annotations(
    owner="your-org",
    slug="migrated-from-cvat",
    format="coco",
    file_path="path/to/coco/annotations.json"
)

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Label Mapping

CVAT labels map directly to Avala labels. If your CVAT project uses attributes, they will be imported as annotation attributes in Avala.
CVAT ConceptAvala Equivalent
TaskProject
JobWork Batch
LabelLabel
AttributeAnnotation Attribute
TrackTracking Annotation
TagClassification Label

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Migrating from Labelbox

Labelbox exports annotations in its native JSON format or COCO format.

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Step 1: Export from Labelbox

  1. Navigate to your Labelbox project
  2. Go to Export and select Export v2
  3. Choose your export format:
    • Labelbox JSON (native format with all metadata)
    • COCO (recommended for Avala import)
  4. Download the export file

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Step 2: Transform and Import

If exporting in COCO format, you can import directly. For Labelbox JSON format, convert to COCO first: 
import avala
import json
import os

client = avala.Client(api_key=os.environ["AVALA_API_KEY"])

# For COCO exports, import directly
client.datasets.import_annotations(
    owner="your-org",
    slug="migrated-from-labelbox",
    format="coco",
    file_path="path/to/coco/annotations.json"
)

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Label Mapping

Labelbox ConceptAvala Equivalent
ProjectProject
Data RowDataset Item
LabelAnnotation
Tool (Bounding Box)Bounding Box Annotation
Tool (Polygon)Polygon Annotation
Tool (Polyline)Polyline Annotation
Tool (Point)Keypoint Annotation
ClassificationClassification Label
BenchmarkConsensus
ReviewReview

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Migrating from Label Studio

Label Studio supports multiple export formats including COCO, YOLO, and its own JSON format.

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Step 1: Export from Label Studio

  1. Open your Label Studio project
  2. Click Export in the top navigation
  3. Select your format:
    • COCO (recommended for object detection annotations)
    • YOLO (for bounding box annotations)
    • JSON (Label Studio native format)
  4. Download the export

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Step 2: Import to Avala

import avala
import os

client = avala.Client(api_key=os.environ["AVALA_API_KEY"])

# Import COCO annotations from Label Studio export
client.datasets.import_annotations(
    owner="your-org",
    slug="migrated-from-label-studio",
    format="coco",
    file_path="path/to/coco/annotations.json"
)

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Label Mapping

Label Studio ConceptAvala Equivalent
ProjectProject
TaskDataset Item
AnnotationAnnotation
RectangleLabelsBounding Box
PolygonLabelsPolygon
KeyPointLabelsKeypoint
BrushLabelsSegmentation
ChoicesClassification Label

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General Migration Steps

For platforms not listed above, follow this general process:

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1. Export Your Data

Export annotations from your current platform in a standard format. Most platforms support at least one of these:
  • COCO JSON
  • YOLO TXT
  • Pascal VOC XML

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2. Transform to Avala Format

If your export format is not directly supported, transform it to COCO format, which provides the most complete mapping to Avala’s annotation model.

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3. Upload Data to Avala

Create a dataset and upload your images or data files: 
import avala
import os

client = avala.Client(api_key=os.environ["AVALA_API_KEY"])

# Create dataset
dataset = client.datasets.create(
    name="migrated-data",
    data_type="image"
)

# Upload images (SDK handles batching automatically)
client.datasets.upload(
    owner="your-org",
    slug="migrated-data",
    file_paths=["path/to/images/"]
)

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4. Import Annotations

Import the exported annotations into your Avala dataset: 
client.datasets.import_annotations(
    owner="your-org",
    slug="migrated-data",
    format="coco",
    file_path="path/to/annotations.json"
)

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5. Verify and Review

After import, review your data in Mission Control to verify annotations were imported correctly.

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Data Format Mapping

The following table shows how common annotation formats map to Avala’s internal format.
Source FormatAnnotation Types SupportedNotes
COCO JSONBounding boxes, polygons, keypoints, segmentation masksRecommended format. Full metadata support including categories and image info.
YOLO TXTBounding boxesOne text file per image. Class IDs mapped via a separate classes file.
Pascal VOC XMLBounding boxesOne XML file per image. Supports difficulty flags and truncation metadata.
CVAT XMLBounding boxes, polygons, polylines, keypoints, tracksPreserves tracking information and frame-level annotations.
Labelbox JSONAll typesRequires conversion to COCO for import. Contact support for assistance.
Label Studio JSONAll typesRequires conversion to COCO for import.

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Tips for a Smooth Migration

  • Start with a small test: Migrate a small subset of your data first to validate the process before migrating everything.
  • Verify label consistency: Ensure label names match between your source platform and Avala. Mismatched labels will be created as new labels.
  • Preserve metadata: When possible, export with full metadata to retain information like creation dates, annotator assignments, and review statuses.
  • Back up your source data: Keep a copy of your exported data from the original platform until you have verified the migration is complete.
For enterprise migrations involving large datasets (100,000+ items) or custom formats, contact support@avala.ai for dedicated migration assistance.