Data gambar Hello: Melatih model klasifikasi gambar AutoML
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Gunakan konsol Google Cloud untuk melatih model klasifikasi gambar AutoML.
Setelah set data dibuat dan data diimpor, gunakan
konsolGoogle Cloud untuk meninjau gambar pelatihan dan memulai pelatihan
model.
Setiap halaman mengasumsikan bahwa Anda telah menjalankan petunjuk dari halaman sebelumnya dalam tutorial ini.
Meninjau gambar yang diimpor
Setelah set data diimpor, Anda akan diarahkan ke tab Browse. Anda juga dapat mengakses tab ini dengan memilih Datasets dari menu. Pilih annotation set (kumpulan anotasi gambar berlabel tunggal) yang terkait dengan set data baru.
Pilih Create untuk membuka jendela Train new model.
Pilih Select training method, lalu pilih Target dataset jika belum dipilih secara otomatis. Pastikan tombol pilihan radio_button_checkedAutoML dipilih, lalu pilih CONTINUE.
(Opsional) Pilih Define your model, lalu masukkan Model name. Klik CONTINUE.
Pilih Train options. Pilih opsi model sesuai dengan kebutuhan akurasi dan latensi Anda. Jika ingin, aktifkan pelatihan inkremental dan klik CONTINUE.
Pertimbangan pelatihan inkremental meliputi:
Pelatihan inkremental dapat diaktifkan jika ada setidaknya satu model dasar yang telah dilatih dalam project ini dengan objektif yang sama.
Pelatihan inkremental memungkinkan Anda menggunakan model dasar yang ada sebagai titik awal untuk melatih model baru, bukan melatih model baru dari awal sama sekali.
Secara umum, pelatihan inkremental mempercepat jalannya pelatihan dan menghemat waktu.
Model dasar dapat dilatih dari set data yang berbeda.
Pilih Compute and Pricing. Tetapkan anggaran jam kerja node ke 8 jam kerja node. Pilih Start training.
Anggaran jam kerja node adalah waktu maksimum (mungkin sedikit bervariasi) yang digunakan model untuk pelatihan. Nilai ini dikalikan dengan harga per jam node untuk menghitung total biaya pelatihan. Jam pelatihan yang lebih lama akan menghasilkan model yang lebih akurat (hingga batas tertentu), tetapi akan menghasilkan biaya yang lebih tinggi. Untuk tujuan
pengembangan, anggaran rendah tidak masalah, tetapi untuk produksi, penting untuk mencapai
keseimbangan antara biaya dan akurasi.
Pelatihan memerlukan waktu beberapa jam. Notifikasi email akan dikirim setelah pelatihan model selesai.
[[["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: Train an AutoML image classification model\n\nUse the Google Cloud console to train an AutoML image classification model.\nAfter your dataset is created and data is imported, use the\nGoogle Cloud console to review the training images and begin model\ntraining.\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.](/vertex-ai/docs/tutorials/image-classification-automl/dataset)\n\n3. Train an AutoML image classification\n model.\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\nReview imported images\n----------------------\n\nAfter the dataset import, you are taken to the **Browse** tab. You can also access\nthis tab by selecting **Datasets** from the menu. Select the\n**annotation set** (set of single-label image annotations) associated with your\nnew dataset.\n| **Key point:** An *annotation set* is the collection of annotations associated with a data type and a specific objective (image data type, classification objective in this case). For more information about *annotation sets* , see [Creating an annotation\n| set](/vertex-ai/docs/datasets/create-annotation-set).\n\n[Go to the Datasets page](https://console.cloud.google.com/vertex-ai/datasets)\n\nBegin AutoML model training\n---------------------------\n\nChoose one of the following options to begin training:\n\n- Choose **Train new model**.\n\n- Select **Models** from the menu, and select **Create**.\n\n1. [Go to the Models page](https://console.cloud.google.com/vertex-ai/models)\n\n2. Select **Create** to open the **Train new model** window.\n\n3. Select **Select Training method** , and select the **target Dataset**\n if they are not automatically selected. Make sure\n the radio_button_checked**AutoML**\n radio button is selected, and then choose **CONTINUE**.\n\n4. (Optional) Select **Define your model** , and enter the **Model name** . Click **CONTINUE**.\n\n5. Select **Train options** . Select a model option according to your accuracy and latency needs. Optionally, enable incremental training and click **CONTINUE**.\n\n Incremental training considerations follow:\n - Incremental training can be enabled when there is at least one base model that has been trained in this project with the same objective.\n - Incremental training lets you use an existing base model as a starting point to train a new model rather than training a new model from scratch.\n - Incremental training generally helps training to occur faster and saves training time.\n - The base model can be trained from a different dataset.\n\n6. Select **Compute and pricing** . Specify a node-hour budget of **8 node hours** . Select **Start training**.\n\n Node-hour budget is the maximum time (may vary slightly) that the model\n spends training. This value is multiplied by the\n [price per node hour](/vertex-ai/pricing#automl_models)\n to calculate to total training cost. More training hours results in a more\n accurate (up to a point) model but results in a higher cost. For development\n purposes, a low budget is fine but for production it's important to strike a\n balance between cost and accuracy.\n\nTraining takes several hours. An email notification is sent when the model training completes.\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/image-classification-automl/error-analysis) to check the\nperformance of your trained AutoML model and explore ways of making it better.\n\nFollow [Deploy a model to an endpoint and make a prediction](/vertex-ai/docs/tutorials/image-classification-automl/deploy-predict) to deploy your trained AutoML model. An image is sent to the model for prediction."]]
