Sedna
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QUICK START

  • Quick Start

INTRODUCTION

  • Edge Cloud Collaborative AI Framework
  • Dataset and Model
  • Federated Learning
  • Incremental Learning
  • Joint Inference
  • Lifelong Learning
  • Object Search Service
  • Object Tracking Service

DEPLOY

  • Installtion
  • Standalone

EXAMPLES

  • Using Federated Learning Job in Surface Defect Detection Scenario
  • Using Incremental Learning Job in Helmet Detection Scenario
  • Using Joint Inference Service in Helmet Detection Scenario
  • Using Lifelong Learning Job in Thermal Comfort Prediction Scenario

API

  • Sedna Python SDK

Contributing

  • 1. Install Tools
  • 2. Clone the code
  • 3. Set up Kubernetes/KubeEdge(optional)
  • 4. What’s Next?

API REFERENCE

  • lib.sedna
    • lib.sedna.algorithms
      • lib.sedna.algorithms.aggregation
        • lib.sedna.algorithms.aggregation.aggregation
      • lib.sedna.algorithms.hard_example_mining
      • lib.sedna.algorithms.multi_task_learning
      • lib.sedna.algorithms.unseen_task_detect
    • lib.sedna.backend
    • lib.sedna.common
    • lib.sedna.core
    • lib.sedna.datasources
    • lib.sedna.service
    • lib.sedna.__version__

ROADMAP

  • Roadmap
Sedna
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  • lib.sedna.algorithms.aggregation
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© Copyright 2020, Kubeedge. Revision 8b782ae0. Last updated on Sep 02, 2021.

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