Encord Active

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Streamline annotation and reinforcement learning workflows with Encord. Annotate data, improve model quality, and access expert labelers on-demand.0
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What is Encord Active?

Encord is a data platform designed for computer vision teams that aims to streamline annotation and reinforcement learning from human feedback (RLHF) workflows, monitor and evaluate model performance, and curate visual data. It helps AI teams achieve peak model performance by providing a complete data engine for AI model development. With Encord, users can annotate data, gain actionable insights to improve model quality, accelerate labeling services, and create high-quality training data. The platform also offers customizable workflows, advanced labeling ontologies, model-assisted labeling, enhanced quality assurance, and support for various data types. Additionally, Encord provides data-driven insights, quality metrics, active learning workflows, and model performance metrics. Users can access high-quality labelers on-demand through Encord Accelerate and integrate the platform with their existing ML pipelines using dedicated integrations and API/SDK capabilities.

Key Features:

1. Annotation Tools: Encord provides a simple and powerful annotation experience, allowing users to label computer vision data efficiently. It supports various annotation types, including object detection, keypoint skeleton pose, polyline annotation, hanging protocols, instance segmentation, action recognition, frame classifications, and polygons.

2. Customizable Workflows: Users can create fully customized and automated machine learning (ML) pipelines to improve the efficiency and quality of their annotation workforce. Encord supports advanced labeling ontologies, enabling users to create nested classifications and better ground truth data.

3. Model-Assisted Labeling: Encord allows users to label data 10 times faster with automated labeling. It helps train deep learning models and fine-tune foundation models by automating annotation without compromising label quality. Users can also benefit from in-depth performance analytics to optimize workforce efficiency and ensure model excellence.

Use Cases: Encord can be effectively utilized in various situations and problems, such as:

1. Computer Vision Teams: Encord is designed for computer vision teams working on AI model development. It streamlines annotation workflows, improves model quality, and helps manage and curate visual data, enabling teams to reach production AI faster.

2. Data Labeling and Annotation: Encord's annotation tools and workflows are ideal for creating high-quality training data. It assists users in efficiently labeling any computer vision modality, including image, video, medical imagery, and geospatial data.

3. Model Performance Evaluation: Encord's data-driven insights, quality metrics, and model performance metrics are valuable for evaluating and improving model performance. It helps identify labeling errors, uncover outliers, and diagnose model issues, leading to better models and faster development.

Encord is a comprehensive data platform that empowers computer vision teams to streamline their workflows, improve model performance, and curate visual data effectively. With its annotation tools, customizable workflows, model-assisted labeling, and advanced analytics, Encord provides the necessary tools and insights to accelerate AI model development. Whether it's creating high-quality training data, evaluating model performance, or accessing expert labelers on-demand, Encord offers a powerful solution for all computer vision needs.

More information on Encord Active

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Top 5 Countries

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Traffic Sources

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Updated Date: 2024-04-30
Encord Active was manually vetted by our editorial team and was first featured on September 4th 2024.
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