Searching the RRID Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
URL: https://segment-anything.com/
Proper Citation: Segment Anything Model (RRID:SCR_023680)
Description: Promptable segmentation system with zero shot generalization to unfamiliar objects and images, without need for additional training. Github repository provides code for running inference with SegmentAnything Model, links for downloading trained model checkpoints, and example notebooks that show how to use the model.
Abbreviations: SAM
Synonyms: Meta Segment Anything Model
Resource Type: software resource
Defining Citation: DOI:10.48550/arXiv.2304.02643
Keywords: Meta AI, segmentation system,
Expand AllWe found {{ ctrl2.mentions.all_count }} mentions in open access literature.
We have not found any literature mentions for this resource.
We are searching literature mentions for this resource.
Most recent articles:
{{ mention._source.dc.creators[0].familyName }} {{ mention._source.dc.creators[0].initials }}, et al. ({{ mention._source.dc.publicationYear }}) {{ mention._source.dc.title }} {{ mention._source.dc.publishers[0].name }}, {{ mention._source.dc.publishers[0].volume }}({{ mention._source.dc.publishers[0].issue }}), {{ mention._source.dc.publishers[0].pagination }}. (PMID:{{ mention._id.replace('PMID:', '') }})
Check
for all resource mentions.
A list of researchers who have used the resource and an author search tool
A list of researchers who have used the resource and an author search tool. This is available for resources that have literature mentions.
No rating or validation information has been found for Segment Anything Model.
No alerts have been found for Segment Anything Model.
Source: SciCrunch Registry
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
Email: info_at_ dknet.org
