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Encord

Encord is a computer vision data labeling and model evaluation platform that provides tools and APIs for managing datasets, annotation tasks, labels, and quality workflows at scale. The platform supports multimodal data including images, video, audio, DICOM, point clouds, and text. Encord exposes a REST API and a Python SDK (encord) for programmatically managing projects, datasets, ontologies, storage, and automated labeling workflows. Authentication is handled via SSH private key or service account credentials, with separate US and UK API endpoints available. Encord is used by AI teams in healthcare, autonomous systems, and other industries to accelerate model development by streamlining data curation, annotation, and evaluation pipelines.

2 APIs 0 Features
Computer VisionData LabelingAnnotationMachine LearningModel EvaluationDataset ManagementArtificial IntelligenceImage AnnotationVideo AnnotationDICOMActive LearningQuality Assurance

APIs

Encord Annotate API

REST API and Python SDK for programmatically managing annotation projects, datasets, ontologies, labels, and workflows within the Encord Annotate platform. Supports creating and...

Encord Active API

API for the Encord Active module, which provides model evaluation, data curation, and active learning capabilities. Allows importing model predictions, computing quality metrics...

Semantic Vocabularies

Encord Context

0 classes · 0 properties

JSON-LD

Resources

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Website
Website
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Documentation
Documentation
👥
GitHubOrganization
GitHubOrganization
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LinkedIn
LinkedIn
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X
X
📰
Blog
Blog
💰
Pricing
Pricing
🟢
StatusPage
StatusPage
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Plans
Plans
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RateLimits
RateLimits
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FinOps
FinOps
📰
BlogCatalog
BlogCatalog
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JSONLDContext
JSONLDContext

Sources

apis.yml Raw ↑
aid: encord
name: Encord
type: Index
image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/apis.yml
created: 2026-06-12
modified: 2026-06-12
specificationVersion: "0.19"
description: >
  Encord is a computer vision data labeling and model evaluation platform that
  provides tools and APIs for managing datasets, annotation tasks, labels, and
  quality workflows at scale. The platform supports multimodal data including
  images, video, audio, DICOM, point clouds, and text. Encord exposes a REST
  API and a Python SDK (encord) for programmatically managing projects,
  datasets, ontologies, storage, and automated labeling workflows. Authentication
  is handled via SSH private key or service account credentials, with separate
  US and UK API endpoints available. Encord is used by AI teams in healthcare,
  autonomous systems, and other industries to accelerate model development by
  streamlining data curation, annotation, and evaluation pipelines.
tags:
  - Computer Vision
  - Data Labeling
  - Annotation
  - Machine Learning
  - Model Evaluation
  - Dataset Management
  - Artificial Intelligence
  - Image Annotation
  - Video Annotation
  - DICOM
  - Active Learning
  - Quality Assurance

apis:
  - name: Encord Annotate API
    description: >
      REST API and Python SDK for programmatically managing annotation projects,
      datasets, ontologies, labels, and workflows within the Encord Annotate
      platform. Supports creating and retrieving projects and datasets,
      importing and exporting labels, managing automated labeling agents,
      integrating cloud storage (AWS, Azure, GCP), and orchestrating
      annotation workflows.
    image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
    humanURL: https://docs.encord.com/
    baseURL: https://api.encord.com/public
    tags:
      - Annotation
      - Projects
      - Datasets
      - Labels
      - Workflows
    properties:
      - type: Documentation
        url: https://docs.encord.com/
      - type: PythonSDK
        url: https://pypi.org/project/encord/
      - type: GitHubRepository
        url: https://github.com/encord-team/encord-client-python

  - name: Encord Active API
    description: >
      API for the Encord Active module, which provides model evaluation, data
      curation, and active learning capabilities. Allows importing model
      predictions, computing quality metrics, running embedding-based similarity
      search, and surfacing the most valuable data for labeling. Supports
      performance metrics including mAP, mAR, F1, True/False Positive and
      Negative counts across classes and IoU thresholds.
    image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
    humanURL: https://encord.com/active/
    baseURL: https://api.encord.com/public
    tags:
      - Model Evaluation
      - Data Curation
      - Active Learning
      - Quality Metrics
      - Embeddings
    properties:
      - type: Documentation
        url: https://docs.encord.com/sdk-documentation/api-active/sdk-active-collections
      - type: GitHubRepository
        url: https://github.com/encord-team/encord-active

common:
  - type: Website
    url: https://encord.com/
  - type: Documentation
    url: https://docs.encord.com/
  - type: GitHubOrganization
    url: https://github.com/encord-team
  - type: LinkedIn
    url: https://www.linkedin.com/company/encord-team
  - type: X
    url: https://x.com/encord_team
  - type: Blog
    url: https://encord.com/blog/
  - type: Pricing
    url: https://encord.com/pricing/
  - type: StatusPage
    url: https://status.encord.com/
  - type: Plans
    url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/plans/encord-plans-pricing.yml
  - type: RateLimits
    url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/rate-limits/encord-rate-limits.yml
  - type: FinOps
    url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/finops/encord-finops.yml
  - type: BlogCatalog
    url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/blogs/blogs.json
  - type: JSONLDContext
    url: https://raw.githubusercontent.com/api-evangelist/encord/refs/heads/main/json-ld/encord-context.jsonld

maintainers:
  - FN: Kin Lane
    email: [email protected]