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AutoML Vision Edge
plat_iosplat_android
Buat model klasifikasi gambar kustom dari data pelatihan Anda sendiri dengan AutoML Vision Edge.
Jika Anda ingin mengenali konten gambar, salah satu opsinya adalah menggunakan API pelabelan gambar di perangkat atau API deteksi objek di perangkat ML Kit.
Model yang digunakan oleh API ini didesain untuk tujuan penggunaan umum dan dilatih untuk mengenali konsep yang paling umum ditemukan dalam foto.
Jika Anda membutuhkan model pelabelan gambar atau deteksi objek yang lebih terspesialisasi, yang mencakup domain konsep yang lebih sempit secara lebih mendetail, misalnya model untuk membedakan spesies bunga atau jenis makanan, Anda dapat menggunakan Firebase ML dan AutoML Vision Edge untuk melatih model dengan gambar dan kategori Anda sendiri. Model kustom dilatih di Google Cloud, dan setelah siap, model tersebut sepenuhnya digunakan di perangkat.
Otomatis melatih model pelabelan gambar dan deteksi objek kustom untuk mengenali label yang penting bagi Anda, dengan menggunakan data pelatihan Anda.
Hosting model bawaan
Hosting model Anda dengan Firebase, lalu muat saat run time. Dengan menghosting model di Firebase, Anda dapat memastikan pengguna memiliki model terbaru tanpa merilis versi baru aplikasi.
Selain itu, Anda juga dapat memaketkan model dengan aplikasi Anda sehingga langsung tersedia setelah diinstal.
Alur implementasi
Susun data pelatihan
Susun set data untuk contoh setiap label yang Anda inginkan agar dikenali model Anda.
Latih model baru
Di Google Cloud console, impor data pelatihan Anda dan gunakan untuk melatih model baru.
Gunakan model di aplikasi Anda
Paketkan model dengan aplikasi Anda atau download dari Firebase ketika diperlukan. Lalu, gunakan model tersebut untuk memberi label pada gambar di perangkat.
Harga & Batas
Untuk melatih model kustom dengan AutoML Vision Edge, Anda harus menggunakan paket bayar sesuai penggunaan (Blaze).
[null,null,["Terakhir diperbarui pada 2025-08-08 UTC."],[],[],null,["AutoML Vision Edge \nplat_ios plat_android \nCreate custom image classification models from your own training data with AutoML Vision Edge.\n\nIf you want to recognize contents of an image, one option is to use ML Kit's\n[on-device image labeling API](https://developers.google.com/ml-kit/vision/image-labeling)\nor [on-device object detection API](https://developers.google.com/ml-kit/vision/object-detection).\nThe models used by these APIs are built for general-purpose use, and are trained\nto recognize the most commonly-found concepts in photos.\n\nIf you need a more specialized image labeling or object detection model, covering a narrower domain\nof concepts in more detail---for example, a model to distinguish between\nspecies of flowers or types of food---you can use Firebase ML and AutoML\nVision Edge to train a model with your own images and categories. The custom\nmodel is trained in Google Cloud, and once the model is ready, it's used fully\non the device.\n| Firebase ML's AutoML Vision Edge features are deprecated. Consider using [Vertex AI](https://cloud.google.com/vertex-ai/docs/beginner/beginners-guide) to automatically train ML models, which you can either [export as TensorFlow\n| Lite models](https://cloud.google.com/vertex-ai/docs/export/export-edge-model) for on-device use or [deploy for cloud-based\n| inference](https://cloud.google.com/vertex-ai/docs/predictions/overview).\n\n[Get started with image labeling](/docs/ml/ios/train-image-labeler)\n[Get started with object detection](/docs/ml/android/train-object-detector)\n\nKey capabilities\n\n|---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Train models based on your data | Automatically train custom image labeling and object detection models to recognize the labels you care about, using your training data. |\n| Built-in model hosting | Host your models with Firebase, and load them at run time. By hosting the model on Firebase, you can make sure users have the latest model without releasing a new app version. And, of course, you can also bundle the model with your app, so it's immediately available on install. |\n\n| **Running AutoML models in the cloud**\n|\n| These pages only discuss generating mobile-optimized models intended to run\n| on the device. However, for models with many thousands of labels or when\n| significantly higher accuracy is required, you might want to run a\n| server-optimized model in the cloud instead, which you can do by calling the\n| Cloud AutoML Vision APIs directly. See\n| [Making an\n| online prediction](https://cloud.google.com/vision/automl/docs/predict).\n|\n| Note that unlike running AutoML Vision Edge models on device, running a\n| cloud-based AutoML model is billed per invocation.\n\nImplementation path\n\n|---|---------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| | Assemble training data | Put together a dataset of examples of each label you want your model to recognize. |\n| | Train a new model | In the Google Cloud console, import your training data and use it to train a new model. |\n| | Use the model in your app | Bundle the model with your app or download it from Firebase when it's needed. Then, use the model to label images on the device. |\n\nPricing \\& Limits\n\nTo train custom models with AutoML Vision Edge, you must be on the pay-as-you-go\n(Blaze) plan.\n| **Important:** You can no longer train models with AutoML Vision Edge while on the Spark plan. If you previously trained models while on the Spark plan, your training data and trained models are still accessible from the Firebase console in read-only mode. If you want to keep this data download it before March 1, 2021.\n\n| Datasets | Billed according to [Cloud Storage rates](https://cloud.google.com/storage/pricing) |\n| Images per dataset | 1,000,000 |\n| Training hours | No per-model limit |\n|--------------------|-------------------------------------------------------------------------------------|\n\nNext steps\n\n- Learn how to [train an image labeling model](/docs/ml/train-image-labeler).\n- Learn how to [train an object detection model](/docs/ml/train-object-detector)."]]
