Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Pelabelan Gambar
plat_iosplat_android
Dengan API pelabelan gambar pada Cloud Vision, Anda dapat mengenali entitas dalam gambar tanpa harus memberikan metadata kontekstual tambahan apa pun.
Pelabelan gambar memberi Anda data tentang konten gambar. Saat menggunakan API tersebut, Anda akan mendapatkan daftar entitas yang dikenali: orang, benda, tempat, aktivitas, dan sebagainya. Setiap label yang ditemukan dilengkapi dengan skor yang menunjukkan keyakinan model ML atas relevansinya. Berbekal informasi ini, Anda dapat melakukan tugas-tugas seperti pembuatan metadata otomatis dan moderasi konten.
API pelabelan gambar Firebase ML didukung oleh kemampuan pemahaman gambar Google Cloud yang terdepan di industrinya, yang dapat mengelompokkan gambar dengan 10.000+ label dalam berbagai kategori. (Lihat di bawah.)
Selain deskripsi teks dari setiap label yang ditampilkan Firebase ML, ID entitas Pustaka Pengetahuan Google dari label juga ditampilkan. ID ini adalah string yang secara unik mengidentifikasi entitas yang diwakili oleh label, dan juga merupakan ID yang digunakan oleh Knowledge Graph Search API. Anda dapat menggunakan string ini untuk mengidentifikasi entitas di seluruh bahasa, dan secara terpisah dari pemformatan deskripsi teks.
Penggunaan terbatas tanpa biaya
Tanpa biaya untuk 1.000 penggunaan pertama fitur ini per bulan: lihat Harga
Contoh label
API pelabelan gambar mendukung 10.000+ label, termasuk contoh berikut ini dan banyak lagi:
Kategori
Contoh label
Kategori
Contoh label
Seni & hiburan
Sculpture Musical Instrument Dance
Benda astronomi
Comet Galaxy Star
Bisnis & industri
Restaurant Factory Airline
Warna
Red Green Blue
Desain
Floral Pattern Wood Stain
Minuman
Coffee Tea Milk
Acara
Meeting Picnic Vacation
Karakter fiksi
Santa Claus Superhero Mythical creature
Makanan
Casserole Fruit Potato chip
Rumah & taman
Laundry basket Dishwasher Fountain
Aktivitas
Wedding Dancing Motorsport
Bahan
Ceramic Textile Fiber
Media
Newsprint Document Sign
Mode transportasi
Aircraft Motorcycle Subway
Pekerjaan
Actor Florist Police
Organisme
Plant Animal Fungus
Organisasi
Government Club College
Tempat
Airport Mountain Tent
Teknologi
Robot Computer Solar panel
Benda
Bicycle Pipe Doll
Hasil contoh
Foto: Clément Bucco-Lechat / Wikimedia Commons / CC BY-SA 3.0
[null,null,["Terakhir diperbarui pada 2025-08-08 UTC."],[],[],null,["# Image Labeling\n==============\n\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|--------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\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\n### Example 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\n---------------\n\nPhoto: 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 |"]]
