Pengantar data gambar: Membuat set data klasifikasi gambar dan mengimpor gambar
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Gunakan konsol Google Cloud untuk membuat set data klasifikasi gambar.
Setelah set data dibuat, gunakan CSV yang menunjuk ke gambar di bucket Cloud Storage publik untuk mengimpor gambar tersebut ke dalam set data.
Setiap halaman mengasumsikan bahwa Anda telah menjalankan petunjuk
dari halaman tutorial sebelumnya.
File input data gambar
File gambar yang Anda gunakan dalam tutorial ini berasal dari set data bunga yang digunakan dalam postingan blog Tensorflow ini.
Gambar input ini disimpan di bucket Cloud Storage publik. Bucket yang dapat diakses secara publik ini juga berisi file CSV yang Anda gunakan untuk impor data.
File ini memiliki dua kolom: kolom pertama mencantumkan URI gambar di Cloud Storage, dan kolom kedua berisi label gambar. Di bawah ini Anda dapat melihat beberapa baris contoh:
Membuat set data klasifikasi gambar dan mengimpor data
Buka konsol Google Cloud untuk memulai proses pembuatan set data dan melatih model klasifikasi gambar Anda.
Saat diminta, pastikan Anda memilih project yang Anda gunakan untuk
bucket Cloud Storage Anda.
Dari halaman Get started with Vertex AI, klik
Create dataset.
Tentukan nama untuk set data ini (opsional).
Di tab Image di bagian "Select a data type and objectve", pilih
opsi pilihan
radio_button_checkedKlasifikasi gambar (Label tunggal). Di menu drop-down Region, pilih US Central.
Pilih Create untuk membuat set data kosong. Setelah memilih 'Create', Anda akan
melanjutkan ke jendela impor data.
Pilih radio_button_checkedSelect import files from Cloud Storage dan tentukan Cloud Storage URI dari file CSV dengan lokasi gambar dan data label. Untuk panduan memulai ini, file CSVnya ada di gs://cloud-samples-data/ai-platform/flowers/flowers.csv. Salin dan tempel
baris berikut ke kolom "Import file path":
Klik Lanjutkan untuk memulai impor gambar. Proses impor memerlukan
waktu beberapa menit. Setelah selesai, Anda akan diarahkan ke halaman berikutnya yang menampilkan semua gambar yang diidentifikasi untuk set data Anda, baik gambar berlabel maupun tidak berlabel.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[],[],null,["# Hello image data: Create an image classification dataset and import images\n\nUse the Google Cloud console to create an image classification dataset.\nAfter your dataset is created, use a CSV pointing to images in a public\nCloud Storage bucket to import those images into the dataset.\n\nThis tutorial has several pages:\n\n1. [Set up your project and environment.](/vertex-ai/docs/tutorials/image-classification-automl)\n\n2. Create an image classification dataset, and\n import images.\n\n3. [Train an AutoML image classification\n model.](/vertex-ai/docs/tutorials/image-classification-automl/training)\n\n4. [Evaluate and analyze model performance.](/vertex-ai/docs/tutorials/image-classification-automl/error-analysis)\n\n5. [Deploy a model to an endpoint, and send a\n prediction.](/vertex-ai/docs/tutorials/image-classification-automl/deploy-predict)\n\n6. [Clean up your project.](/vertex-ai/docs/tutorials/image-classification-automl/cleanup)\n\nEach page assumes that you have already performed the instructions from the\nprevious pages of the tutorial.\n\nImage data input file\n---------------------\n\n| **Key point** : A single dataset can be used for multiple objectives. This tutorial focuses on *image classification* (applying a label to an image), but the same data could be used for another objective, such as *object detection* (object identification and labeling).\n\nThe image files you use in this tutorial are from the flower dataset used in\nthis [Tensorflow blog post](https://cloud.google.com/blog/products/gcp/how-to-classify-images-with-tensorflow-using-google-cloud-machine-learning-and-cloud-dataflow).\nThese input images are stored in a public Cloud Storage bucket. This\npublicly-accessible bucket also contains a CSV file you use for data import.\nThis file has two columns:\nthe first column lists an image's URI in Cloud Storage, and the second\ncolumn contains the image's label. Below you can see some sample rows:\n\n`gs://cloud-samples-data/ai-platform/flowers/flowers.csv`: \n\n gs://cloud-samples-data/ai-platform/flowers/daisy/10559679065_50d2b16f6d.jpg,daisy\n gs://cloud-samples-data/ai-platform/flowers/dandelion/10828951106_c3cd47983f.jpg,dandelion\n gs://cloud-samples-data/ai-platform/flowers/roses/14312910041_b747240d56_n.jpg,roses\n gs://cloud-samples-data/ai-platform/flowers/sunflowers/127192624_afa3d9cb84.jpg,sunflowers\n gs://cloud-samples-data/ai-platform/flowers/tulips/13979098645_50b9eebc02_n.jpg,tulips\n\nCreate an image classification dataset and import data\n------------------------------------------------------\n\nVisit the [Google Cloud console](https://console.cloud.google.com/vertex-ai/)\nto begin the process of creating your dataset and training your image\nclassification model.\n\nWhen prompted, make sure to select the project that you used for your Cloud\nStorage bucket.\n\n1. From the Get started with Vertex AI page, click\n **Create dataset**.\n\n2. Specify a name for this dataset (optional).\n\n3. In the Image tab of the \"Select a data type and objective\" section, choose\n the\n radio_button_checked**Image classification (Single-label)**\n radio option. In the Region drop-down menu select **US Central**.\n\n4. Select **Create** to create the empty dataset. After selecting Create you\n will advance to the data import window.\n\n5. Select the radio_button_checked**Select\n import files from Cloud Storage** and specify the Cloud Storage\n URI of the CSV file with the image location and label data. For this\n quickstart, the CSV file is at\n `gs://cloud-samples-data/ai-platform/flowers/flowers.csv`. Copy and paste\n the following into the \"Import file path\" field:\n\n -\n\n ```\n cloud-samples-data/ai-platform/flowers/flowers.csv\n ```\n\n6. Click **Continue** to begin image import. The import process takes a\n few minutes. When it completes, you are taken to the next page that shows\n all of the images identified for your dataset, both labeled and\n unlabeled images.\n\n | When using the indicated flower dataset, you will see several warning alerts. This is purposeful, to show you error messages you may encounter with your own data.\n\n \u003cbr /\u003e\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/image-classification-automl/training) to start an\nAutoML model training job."]]
