RFP QuestBeta
ClosedStage · contract

Cefas

CEFAS23-100 Contract for Ghanaian image annotation of marine litter images under the Ocean Country Partnership Programme (OCPP)

IT ServicesCPV 72000000 72300000 72310000 79000000 79900000 79960000
Value£47k
Deadline8 Aug 2023
Published14 Sept 2023
RegionUK-wide
Timeline
Published 14 Sept 2023ClosedCloses 8 Aug 2023
Contract value in context
£47ktotal contract value
median £120k
this tender£0£3.5m

This sits below the typical range for IT Services contracts — a smaller, more accessible award. Based on 36,449 valued IT Services tenders in our corpus.

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The brief

The Ocean Country Partnership Programme (OCPP) was announced in 2021 as a key bilateral aid programme under the £500m Blue Planet Fund.

OCPP objectives are to support countries to tackle marine pollution, support sustainable seafood practices and establish designated, well-managed and enforced Marine Protected Areas (MPAs).

The overall aim of this project is to produce a machine learning algorithm capable of identifying at least 89 marine litter items as defined by international protocols.

The main output of this work will therefore be one of the largest and most detailed databases of high-quality annotated images of marine litter in the world.

The supplier will annotate approximately 7,000 images, collected from 8 beaches along the Ghanaian coast during the wet and dry season.

The supplier will use a photo guide provided to them by Cefas, which will contain example images for the 89 litter categories.

In addition, the supplier will assist Cefas to perform inter-rater assessments at the beginning, middle and end of the contract to better understand human error in litter annotations.

Key requirements

What the supplier must deliver

01

OCPP objectives are to support countries

OCPP objectives are to support countries to tackle marine pollution, support sustainable seafood practices and establish designated, well-managed and enforced Marine Protected Areas (MPAs).

02

The overall aim of this project is

The overall aim of this project is to produce a machine learning algorithm capable of identifying at least 89 marine litter items as defined by international protocols.

Derived from the notice text — always confirm against the original documents.

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Source & provenance
OCID
81139f0d-dce3-47de-8df9-14fb8ea0dcb3
Stage
contract · Contract
Source
Contracts Finder
Buyer ref
CF-0131800D8d000003VQwdEAG
View the original notice on Contracts Finder

Contains public sector information licensed under the Open Government Licence v3.0. Source data © Crown copyright.

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