{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["17(10)"],"submitter":["Fu L"],"pubmed_abstract":["Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation "],"journal":["PloS one"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9536640"],"repository":["biostudies-literature"],"pubmed_title":["Lightweight individual cow identification based on Ghost combined with attention mechanism"],"pmcid":["PMC9536640"],"pubmed_authors":["Hu T","Li S","Mu Y","Guo Y","Ni R","Fu L","Kong S","Pang H","Sun Y","Gong H"],"additional_accession":[]},"is_claimable":false,"name":"Lightweight individual cow identification based on Ghost combined with attention mechanism","description":"Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-19T17:38:11.155Z","creation":"2025-02-19T00:27:19.406Z"},"accession":"S-EPMC9536640","cross_references":{}}