[null,null,["最后更新时间 (UTC)：2025-08-07。"],[],[],null,["Image Labeling \nplat_ios plat_android \n\nWith ML Kit's image labeling APIs, you can recognize entities in an\nimage without having to provide any additional contextual metadata, using either\nan on-device API or a cloud-based API.\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[iOS](/docs/ml-kit/ios/label-images)\n[Android](/docs/ml-kit/android/label-images)\n\nIf you're a Flutter developer, you might be interested in\n[FlutterFire](https://github.com/FirebaseExtended/flutterfire/tree/master/packages/firebase_ml_vision),\nwhich includes a plugin for Firebase's ML Vision APIs.\n| **Want to label images with your own categories?** Train your own image labeling models with [AutoML Vision Edge](/docs/ml-kit/automl-image-labeling).\n| This is a beta release of ML Kit for Firebase. This API might be changed in backward-incompatible ways and is not subject to any SLA or deprecation policy.\n\nChoose between on-device and Cloud APIs\n\nExample on-device labels\n\nThe device-based API supports 400+ labels, such as the following examples:\n\nExample cloud labels\n\nThe cloud-based API supports 10,000+ labels, such as the following examples:\n\nGoogle Knowledge Graph entity IDs\n\nIn addition the text description of each label that ML Kit returns, it also\nreturns the label's Google Knowledge Graph entity ID. This ID is a string that\nuniquely identifies the entity represented by the label, and is the same ID used\nby the [Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).\nYou can use this string to identify an entity across languages, and\nindependently of the formatting of the text description.\n\nExample results Photo: Clément Bucco-Lechat / Wikimedia Commons / CC BY-SA 3.0"]]
