WP3.5.1 - Development of Datasets for Machine Learning - LongOps
key details
| Value | £255,000 |
|---|---|
| Status | complete |
| Category (CPV) | 48000000 +27 more |
| Region | South East |
| Deadline | 29 Apr 2022 |
| Procedure | open |
| OCID | ocds-h6vhtk-02d476 |
Awards (2)
This procurement was awarded to 1 suppliers. Values shown are per-award; the notice total is £255,000.
| Supplier | Value | Date | Status |
|---|---|---|---|
| Jacobs Clean Energy Limited | £254,958 | 14 Aug 2022 | active |
| Jacobs Clean Energy Limited | · | · | active |
description
As part of the LongOps project, it is required to develop decision support tools capable of helping de-risking operations and supporting training in teleoperated decommissioning tasks involving the detection and pose estimation of objects and debris within nuclear facilities. In recent years the use of Machine Learning (ML) techniques such as object detection, object classification, segmentation, semantic information extraction from images and videos, denoising, etc. has yielded important results in terms of visual and other sensory perception. However, this requires significant volumes of relevant, high-quality annotated data to achieve adequate levels of performance.
UKAEA wishes to procure the collection, evaluation, processing, and annotation of data gathered from nuclear-related sites and/or similar scenarios.
documents
- https://uk.eu-supply.com/app/rfq/rwlentrance_s.asp?PID=44924&B=UK
- https://uk.eu-supply.com/ctm/Company/CompanyInformation/Index/72814
- https://www.contractsfinder.service.gov.uk/Notice/789e924e-74cd-43eb-820e-d97ded437bc1 , tenderNotice
Documents are linked, not mirrored. They are served by the publishing authority and may require registration.
notice history
5 notices published against this procurement.
| Published | Type | Regime | Notice |
|---|---|---|---|
| 13 Aug 2021 | Prior information notice (F01) | Earlier regulations | 019719-2021 |
| 18 Mar 2022 | Contract notice (F02) | Earlier regulations | 007431-2022 |
| 18 Mar 2022 | Tender (tender) | · | 789e924e-74cd-43eb-820e-d97ded437bc1-519665 |
| 15 Aug 2022 | Contract award notice (F03) | Earlier regulations | 022432-2022 |
| 15 Aug 2022 | Award (award) | · | 09576c74-3227-448c-8635-6837cdada4e2-563807 |
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source
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