Draft:Encord
Submission declined on 14 February 2024 by Johannes Maximilian (talk).
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Submission declined on 25 January 2024 by DoubleGrazing (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by DoubleGrazing 7 months ago.
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This article may have been created or edited in return for undisclosed payments, a violation of Wikipedia's terms of use. It may require cleanup to comply with Wikipedia's content policies, particularly neutral point of view. (January 2024) |
Encord is a software company that specialises in developing tools and infrastructure for artificial intelligence and deep learning applications, focused on computer vision.
History
[edit]Encord was co-founded as Cord in 2020 by Ulrik Stig Hansen and Eric Landau and went through the Y Combinator accelerator programme in winter 2021.[2]
In June 2021, Encord announced its $4.5M Seed financing led by CRV and Y Combinator.[3] In October the same year, Encord announced its $12.5M Series A financing led by CRV and Y Combinator.[4]
The company launched its first product focused on automating labeled data creation for computer vision applications in April 2022[5] and a data quality assessment tool soon after in June 2022.[6]
In January of 2023 Encord launched Encord Active, a tool to improve AI models in production through improved model observability and data quality analytics[7]
Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.
- Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling.
AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA.
Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines.
- Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality.
Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale.
- Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors.
Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. - Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats).
References
[edit]- ^ "Best Image Annotation Tools for Computer Vision [Updated 2024]".
- ^ "Encord: All the tools you need to build better vision models, faster". Y Combinator. Retrieved 2024-01-25.
- ^ Wiggers, Kyle (2021-06-15). "Cord raises $4.5M to automate computer vision annotation processes". VentureBeat. Retrieved 2024-01-25.
- ^ "Cord Continues Record Growth With Its New Micro-model Approach, Automating an Archaic Annotation Process With $12.5M in New Funding". BusinessWire. 2021-10-13. Retrieved 2024-01-25.
- ^ Betuel, Emma (2022-04-08). "Encord launched an AI-assisted labeling program". TechCrunch. Retrieved 2024-01-25.
- ^ Plumb, Taryn (2022-06-01). "Encord tackles growing problem of unlabeled data". VentureBeat. Retrieved 2024-01-25.
- ^ "Encord offers ML toolkit for computer vision apps". Computer Weekly. Retrieved 2024-01-25.
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