Cloud Vision의 이미지 라벨 지정 API를 사용하면 문맥 메타데이터를 추가하지 않고도 이미지 속 항목을 인식할 수 있습니다.
이미지 라벨을 지정하면 이미지 콘텐츠에 대한 정보를 파악할 수 있습니다. API를 사용하면 인물, 사물, 장소, 활동 등 인식된 항목의 목록을 가져올 수 있습니다. 검색된 각 라벨에는 ML 모델의 관련성을 나타내는 신뢰도 점수가 표시됩니다. 이 정보를 근거로 자동 메타데이터 생성 및 콘텐츠 검토 등의 태스크를 수행할 수 있습니다.
Firebase ML은 각 라벨의 텍스트 설명 외에 라벨의 Google 지식 그래프 항목 ID도 반환합니다. 이 ID는 라벨을 통해 제공되는 항목을 고유하게 식별하는 문자열이며 Knowledge Graph Search API에서 사용하는 ID와 동일합니다. 이 문자열을 사용하면 텍스트 설명의 형식과 언어에 관계없이 항목을 식별할 수 있습니다.
[null,null,["최종 업데이트: 2025-08-05(UTC)"],[],[],null,["Image Labeling \nplat_ios plat_android \n\nWith Cloud Vision's image labeling APIs, you can recognize entities in\nan image without having to provide any additional contextual metadata.\n\nImage labeling gives you insight into the content of images. When you use the\nAPI, you get a list of the entities that were recognized: people, things,\nplaces, activities, and so on. Each label found comes with a score that\nindicates the confidence the ML model has in its relevance. With this\ninformation, you can perform tasks such as automatic metadata generation\nand content moderation.\n\n\u003cbr /\u003e\n\nReady to get started? Choose your platform:\n\n[iOS+](/docs/ml/ios/label-images)\n[Android](/docs/ml/android/label-images)\n\n\u003cbr /\u003e\n\n| **Want to label images with your own categories?** Train your own image labeling models with [AutoML Vision Edge](/docs/ml/automl-image-labeling).\n| **Looking for on-device image labeling?** Try the [standalone ML Kit library](https://developers.google.com/ml-kit/vision/image-labeling).\n\nKey capabilities\n\n|--------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| High-accuracy image labeling | Firebase ML's image labeling API is powered by Google Cloud's industry-leading image understanding capability, which can classify images with 10,000+ labels in many categories. (See below.) Try it yourself with the [Cloud Vision API demo](https://cloud.google.com/vision/docs/drag-and-drop). |\n| Knowledge Graph entity support | In addition the text description of each label that Firebase ML returns, it also returns the label's Google Knowledge Graph entity ID. This ID is a string that uniquely identifies the entity represented by the label, and is the same ID used by the [Knowledge Graph Search API](https://developers.google.com/knowledge-graph/). You can use this string to identify an entity across languages, and independently of the formatting of the text description. |\n| Limited no-cost use | No-cost for first 1000 uses of this feature per month: see [Pricing](/pricing) |\n\nExample labels\n\nThe image labeling API supports 10,000+ labels, including the following examples\nand many more:\n\n| Category | Example labels | Category | Example labels |\n|------------------------|------------------------------------------|----------------------|-----------------------------------------------|\n| Arts \\& entertainment | `Sculpture` `Musical Instrument` `Dance` | Astronomical objects | `Comet` `Galaxy` `Star` |\n| Business \\& industrial | `Restaurant` `Factory` `Airline` | Colors | `Red` `Green` `Blue` |\n| Design | `Floral` `Pattern` `Wood Stain` | Drink | `Coffee` `Tea` `Milk` |\n| Events | `Meeting` `Picnic` `Vacation` | Fictional characters | `Santa Claus` `Superhero` `Mythical creature` |\n| Food | `Casserole` `Fruit` `Potato chip` | Home \\& garden | `Laundry basket` `Dishwasher` `Fountain` |\n| Activities | `Wedding` `Dancing` `Motorsport` | Materials | `Ceramic` `Textile` `Fiber` |\n| Media | `Newsprint` `Document` `Sign` | Modes of transport | `Aircraft` `Motorcycle` `Subway` |\n| Occupations | `Actor` `Florist` `Police` | Organisms | `Plant` `Animal` `Fungus` |\n| Organizations | `Government` `Club` `College` | Places | `Airport` `Mountain` `Tent` |\n| Technology | `Robot` `Computer` `Solar panel` | Things | `Bicycle` `Pipe` `Doll` |\n\nExample results Photo: Clément Bucco-Lechat / Wikimedia Commons / CC BY-SA 3.0\n\n| Label | Knowledge Graph entity ID | Confidence |\n|-------------------------|---------------------------|------------|\n| sport venue | /m/0bmgjqz | 0.9860726 |\n| player | /m/02vzx9 | 0.9797604 |\n| stadium | /m/019cfy | 0.9635762 |\n| soccer specific stadium | /m/0404y4 | 0.95806926 |\n| football player | /m/0gl2ny2 | 0.9510419 |\n| sports | /m/06ntj | 0.9253524 |\n| soccer player | /m/0pcq81q | 0.9033665 |\n| arena | /m/018lrm | 0.8897188 |"]]
