Menafsirkan hasil prediksi dari model klasifikasi gambar
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Setelah meminta prediksi, Vertex AI akan menampilkan hasil berdasarkan
tujuan model Anda. Prediksi klasifikasi gambar label tunggal AutoML
menampilkan kategori label tunggal dan skor keyakinan yang sesuai. Prediksi klasifikasi multi-
label menampilkan kategori beberapa label dan
skor keyakinan yang sesuai.
Skor keyakinan mencerminkan seberapa kuat model Anda mengaitkan setiap
class atau label dengan item pengujian. Makin tinggi angkanya, makin tinggi keyakinan
model bahwa label harus diterapkan ke item tersebut. Anda yang menentukan seberapa tinggi
skor keyakinan agar hasil model dapat diterima.
Penggeser nilai minimum skor
Di konsol Google Cloud , Vertex AI menyediakan penggeser yang
digunakan untuk menyesuaikan nilai minimum keyakinan untuk semua class atau label, atau
setiap class atau label. Penggeser tersedia di halaman detail model di
tab Evaluate. Nilai minimum keyakinan adalah level keyakinan yang
harus dimiliki model untuk menetapkan class atau label ke item pengujian. Saat
menyesuaikan nilai minimum, Anda dapat melihat perubahan presisi dan perolehan
model. Nilai minimum yang lebih tinggi biasanya meningkatkan presisi dan menurunkan perolehan.
Contoh output prediksi batch
Output prediksi klasifikasi gambar AutoML batch disimpan sebagai file
JSON Lines
di bucket Cloud Storage. Setiap baris file JSON Lines
berisi semua kategori anotasi (label) dan skor keyakinan
yang sesuai untuk satu file gambar.
[[["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 2025-09-02 UTC."],[],[],null,["# Interpret prediction results from image classification models\n\nAfter requesting a prediction, Vertex AI returns results based on your model's objective. AutoML single-label image classification predictions return a single label category and its corresponding confidence score. Multi- label classification predictions return multiple label categories and their corresponding confidence scores.\n\n\u003cbr /\u003e\n\n\nThe confidence score communicates how strongly your model associates each\nclass or label with a test item. The higher the number, the higher the model's\nconfidence that the label should be applied to that item. You decide how high\nthe confidence score must be for you to accept the model's results.\n\n\u003cbr /\u003e\n\n#### Score threshold slider\n\n\nIn the Google Cloud console, Vertex AI provides a slider that's\nused to adjust the confidence threshold for all classes or labels, or an\nindividual class or label. The slider is available on a model's detail page in\nthe **Evaluate** tab. The confidence threshold is the confidence level that\nthe model must have for it to assign a class or label to a test item. As you\nadjust the threshold, you can see how your model's precision and recall\nchanges. Higher thresholds typically increase precision and lower recall.\n\n\u003cbr /\u003e\n\n#### Example batch prediction output\n\nBatch AutoML image classification prediction output are stored as\n[JSON Lines](https://jsonlines.org/)\nfiles in Cloud Storage buckets. Each line of the JSON Lines file\ncontains all annotation (label) categories and their corresponding\nconfidence scores for a single image file.\n\n\n| **Note**: The following JSON Lines example includes line breaks for\n| readability. In your JSON Lines files, line breaks are included only after each\n| each JSON object.\n\n\u003cbr /\u003e\n\n\n```\n{\n \"instance\": {\"content\": \"gs://bucket/image.jpg\", \"mimeType\": \"image/jpeg\"},\n \"prediction\": {\n \"ids\": [1, 2],\n \"displayNames\": [\"cat\", \"dog\"],\n \"confidences\": [0.7, 0.5]\n }\n}\n```"]]
