RGBD_fruit_detection_faster-rcnn.pytorch

DOI

This project is a pytorch implementation of a Faster R-CNN for fruit detection suitable with multi-modal images (up to 5 channels). It's based on implementation of: jwyang/faster_rcnn.pytorch, developed based on Pytorch + Numpy

This implementation has been used to train and test the KFuji RGB-DS dataset, which contains images with 3 different modalities: colour (RGB), depth(D), and range-corrected intensity signal (S).

Python, 2.7

Pytorch, 0.2.0

CUDA, 8.0

This software is stored and maintained in the following github repository: https://github.com/GRAP-UdL-AT/RGBD_fruit_detection_faster-rcnn.pytorch

Identifier
DOI https://doi.org/10.34810/data2331
Related Identifier IsSupplementedBy https://doi.org/10.1016/j.compag.2019.05.016
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data2331
Provenance
Creator Gené Mola, Jordi ORCID logo; Vilaplana Besler, Verónica ORCID logo; Rosell Polo, Joan Ramon ORCID logo; Morros Rubió, Josep Ramon (ORCID: 0000-0002-1395-487X); Ruiz Hidalgo, Javier (ORCID: 0000-0001-6774-685X); Gregorio López, Eduard ORCID logo
Publisher CORA.Repositori de Dades de Recerca
Contributor Gené Mola, Jordi; Universitat de Lleida; Centre de Recerca en Agrotecnologia
Publication Year 2025
Rights MIT; info:eu-repo/semantics/openAccess; https://opensource.org/licenses/MIT
OpenAccess true
Contact Gené Mola, Jordi (Universitat de Lleida)
Representation
Resource Type Program source code; Dataset
Format text/x-python; application/octet-stream; text/x-c; text/x-matlab; text/plain; text/plain; charset=US-ASCII; application/x-sh; application/cu-seeme; text/markdown
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Version 1.0
Discipline Agricultural Sciences; Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Construction Engineering and Architecture; Engineering; Engineering Sciences; Life Sciences