AI-assisted multimodal annotation
Label images, video, audio and text with tools suited to each format. SAM 2 assists image segmentation and video object tracking; model predictions can also be imported.
Encord helps AI teams label multimodal data, find gaps in datasets, and review model failures through automated and human review workflows.
Encord is a data platform for teams building and testing AI models. It combines annotation, dataset curation and model evaluation across images, video, audio, text and sensor data. Labeling tools and human review produce training labels, while curation and evaluation help teams find outliers, missing examples and model failures to address in later training.
A typical workflow starts with registering cloud data, defining a labeling ontology and configuring annotation and review stages. Teams can use model predictions or AI-assisted tools to prepare labels, assign human reviewers, inspect results and export annotations. Encord also provides SDK access for data and label operations. It suits ongoing AI data operations where labeling rules, team coordination and feedback from model performance need to stay connected.
Label images, video, audio and text with tools suited to each format. SAM 2 assists image segmentation and video object tracking; model predictions can also be imported.
Define nested labeling ontologies, assign roles and tasks, and use multi-stage human review. Dashboards track project progress and annotator performance.
Use embedding-based search and model-in-the-loop curation to find rare cases and distribution gaps. Dataset tools also include filtering, outlier detection and duplicate-image detection.
Review label and model errors, compare models, and organize rubric-based evaluation, pairwise comparison or RLHF workflows.
Register files from cloud storage, import existing annotations and export labels through platform or SDK workflows. SDK access uses authentication keys.
Set up image or video labeling rules, generate preliminary segments or tracks, and route annotations through human review before exporting labels for training.
Search production datasets for rare cases and inspect model failures, then curate examples that fill gaps in the next training dataset.
Organize labeling across multimodal inputs for robotics or vehicle-perception projects, using the appropriate sensor formats and review workflow for the selected package.
Encord supports images, video, audio, text, documents and specialized inputs such as medical images and 3D sensor data. Starter includes image and video annotation. DICOM and 3D/LiDAR are marked as add-ons in the plan comparison; confirm their availability in your package.
AI-assisted tools and imported model predictions can prepare labels. Encord also supports human review stages, role assignments and quality-control workflows, so teams can inspect and correct those labels.
Starter targets individuals and small prototypes, with image/video tools, ontologies and workflows. Team adds data agents, performance analytics, model evaluation and onboarding. Enterprise adds multiple workspaces, SSO and enterprise support; its deployment options include VPC and on-premises configurations. Enterprise setup is arranged through sales.
Yes. Its SDK supports registering cloud data and importing or exporting labels using access keys. Teams can combine these operations with platform-based annotation, curation and evaluation.
Os dados de tráfego são apenas para referência.
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