Sedna documentation¶
Sedna is an edge-cloud synergy AI project incubated in KubeEdge SIG AI. Benefiting from the edge-cloud synergy capabilities provided by KubeEdge, Sedna can implement across edge-cloud collaborative training and collaborative inference capabilities, such as joint inference, incremental learning, federated learning, and lifelong learning. Sedna supports popular AI frameworks, such as TensorFlow, Pytorch, PaddlePaddle, MindSpore.
Sedna can simply enable edge-cloud synergy capabilities to existing training and inference scripts, bringing the benefits of reducing costs, improving model performance, and protecting data privacy.
- Using Joint Inference Service in Helmet Detection Scenario
- Using Incremental Learning Job in Helmet Detection Scenario
- Using Federated Learning Job in Surface Defect Detection Scenario
- Collaboratively Train Yolo-v5 Using MistNet on COCO128 Dataset
- Using Lifelong Learning Job in Thermal Comfort Prediction Scenario