Cloudinary<\/a>‘s integration with Amazon Rekognition is a straightforward example: an uploaded image can be run through Rekognition’s object and scene detection, and the results attached as tags automatically, subject to a confidence threshold the team sets.<\/p>The gap is between generic and specific recognition. A general-purpose model reliably tells ‘shoe’ apart from ‘bag’, because both are well-represented categories in the datasets these models are trained on. It has much less basis for telling one shoe SKU apart from a visually similar one in the same product line \u2014 that’s a finer distinction than the model was built to make, and treating a low-confidence match as a reliable identification is where these systems get misused.<\/p>","protected":false},"excerpt":{"rendered":"
Computer vision technique that detects and labels physical objects within an image \u2014 a car, a chair, a product box \u2014 typically returning a category and a confidence score, sometimes a bounding box.<\/p>\n","protected":false},"author":0,"featured_media":0,"template":"","meta":{"footnotes":"","faq":[{"question":"What does object recognition do, and how is it distinct from facial recognition or scene classification?","answer":"It detects and labels physical objects within an image \u2014 a car, a chair, a product box \u2014 typically with a category and confidence score, distinct from facial recognition (about people specifically) and scene classification (labeling the whole image, like 'outdoor')."},{"question":"How does Cloudinary's integration with Amazon Rekognition use object recognition?","answer":"An uploaded image can be run through Amazon Rekognition's Auto Tagging add-on at ingestion \u2014 set via Cloudinary's upload API categorization parameter \u2014 which detects objects and scenes and returns a list of categories, each with a confidence score. Results are attached as tags automatically, subject to a confidence threshold the team sets, embedding object recognition directly into the asset processing pipeline rather than requiring a separate manual tagging step."},{"question":"Why is object recognition confidence often much lower for niche or branded objects?","answer":"The underlying models are trained on general-purpose object datasets, not a company's own product catalogue, so they reliably tell 'shoe' apart from 'bag' but have much less basis for telling one shoe SKU apart from a visually similar one in the same product line."},{"question":"What's the risk of trusting object recognition tags for compliance-sensitive labeling?","answer":"Using it without a human check \u2014 for example, to confirm a recalled product doesn't appear in any active marketing asset \u2014 is risky because the model's confidence on that specific SKU can sit far below what a person would assume from a returned 'match.'"},{"question":"What does object recognition let you search a product photography library by?","answer":"Object recognition lets you search a product photography library by the specific objects detected in each image, rather than by photographer-assigned filenames or folder structure. A search for \"chair,\" \"cup,\" or \"plant\" returns every photo where that object was detected, regardless of how the file was named \u2014 without anyone manually tagging each image by hand, since the labels come from automated detection at upload rather than a human cataloguing pass."},{"question":"Can a single photo return multiple object recognition labels at once?","answer":"Yes \u2014 most object recognition systems, including Rekognition-based tagging, are built to detect and return multiple objects from a single image simultaneously rather than being limited to one label per photo. A photo of a living room, for example, could return separate labels for a chair, a table, and a plant in the same pass, each carrying its own independent confidence score rather than a single overall rating for the whole image."}],"checked_date":"2026-08-11","sources":[{"statement":"Cloudinary can automatically classify and tag uploaded images by object and scene category using Amazon Rekognition, with confidence thresholds controlling which detected labels are kept.","source_name":"Cloudinary Documentation \u2014 Amazon Rekognition Auto Tagging Add-on","url":"https:\/\/cloudinary.com\/documentation\/aws_rekognition_auto_tagging_addon","checked":"2026-08-07"}],"kicker":"","fact_checker":0,"reading_time":0,"revisions":[],"seo_title":"Object recognition: detecting and labelling objects in images","seo_description":"","noindex":false,"related":[2471,2569,2455,2462,2469,2456],"definition":"Computer vision technique that detects and labels physical objects within an image \u2014 a car, a chair, a product box \u2014 typically returning a category and a confidence score, sometimes a bounding box.","why":"Object recognition is what lets a product photography library be searched by 'chair' or 'backpack' without anyone typing that tag by hand, and it's the piece platforms like Cloudinary hand off to services such as Amazon Rekognition at ingestion. But recognition confidence for niche or branded objects \u2014 a specific SKU rather than the generic category 'shoe' \u2014 is often much lower, because the underlying models are trained on general-purpose object datasets, not a company's own product catalogue.","example_rows":[],"mistake":"Teams trust object recognition tags for compliance-sensitive labelling \u2014 like confirming a recalled product doesn't appear in any active marketing asset \u2014 without a human check, when the model's confidence on that specific SKU can sit far below what a person would assume from a returned 'match'.","deep_link":""},"silo":[24],"class_list":["post-2460","glossary","type-glossary","status-publish","hentry","silo-glossary"],"_links":{"self":[{"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/glossary\/2460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/glossary"}],"about":[{"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/types\/glossary"}],"version-history":[{"count":5,"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/glossary\/2460\/revisions"}],"predecessor-version":[{"id":3783,"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/glossary\/2460\/revisions\/3783"}],"wp:attachment":[{"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/media?parent=2460"}],"wp:term":[{"taxonomy":"silo","embeddable":true,"href":"https:\/\/picajet.com\/articles\/wp-json\/wp\/v2\/silo?post=2460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}