Menafsirkan hasil prediksi dari model pelacakan objek video
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Setelah meminta prediksi, Vertex AI akan menampilkan hasil berdasarkan
tujuan model Anda. Prediksi dari model pelacakan objek menampilkan waktu dan lokasi objek yang akan dilacak, sesuai dengan label yang Anda tentukan sendiri. Model ini menetapkan skor keyakinan untuk setiap prediksi, yang mengomunikasikan tingkat keyakinan model Anda dalam mengidentifikasi dan melacak objek secara akurat. Makin tinggi angkanya, makin tinggi tingkat keyakinan model terhadap ketepatan prediksi.
Contoh output prediksi batch
Contoh berikut adalah hasil yang diprediksi untuk model yang melacak kucing dan anjing dalam sebuah video. Setiap hasil berisi label (cat atau
dog ) untuk objek yang dilacak, segmen waktu yang menentukan
kapan dan berapa lama objek dilacak, dan kotak pembatas yang
menjelaskan lokasi objek.
[[["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,["# Interpret prediction results from video object tracking models\n\nAfter requesting a prediction, Vertex AI returns results based on your model's objective. Predictions from an object tracking model return time and locations of objects to track, according to your own defined labels. The model assigns a confidence score to each prediction, which communicates how confident your model accurately identified and tracked an object. The higher the number, the higher the model's confidence in the correctness of the prediction.\n\n\u003cbr /\u003e\n\n#### Example batch prediction output\n\nThe following sample is the predicted result for a model that tracks\ncats and dogs in a video. Each result includes a label (`cat` or\n`dog`) for the object being tracked, a time segment that specifies\nwhen and for how long the object is being tracked, and a bounding box that\ndescribes the location of the object.\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\": {\n \"content\": \"gs://bucket/video.mp4\",\n \"mimeType\": \"video/mp4\",\n \"timeSegmentStart\": \"1s\",\n \"timeSegmentEnd\": \"5s\"\n }\n \"prediction\": [{\n \"id\": \"1\",\n \"displayName\": \"cat\",\n \"timeSegmentStart\": \"1.2s\",\n \"timeSegmentEnd\": \"3.4s\",\n \"frames\": [{\n \"timeOffset\": \"1.2s\",\n \"xMin\": 0.1,\n \"xMax\": 0.2,\n \"yMin\": 0.3,\n \"yMax\": 0.4\n }, {\n \"timeOffset\": \"3.4s\",\n \"xMin\": 0.2,\n \"xMax\": 0.3,\n \"yMin\": 0.4,\n \"yMax\": 0.5,\n }],\n \"confidence\": 0.7\n }, {\n \"id\": \"1\",\n \"displayName\": \"cat\",\n \"timeSegmentStart\": \"4.8s\",\n \"timeSegmentEnd\": \"4.8s\",\n \"frames\": [{\n \"timeOffset\": \"4.8s\",\n \"xMin\": 0.2,\n \"xMax\": 0.3,\n \"yMin\": 0.4,\n \"yMax\": 0.5,\n }],\n \"confidence\": 0.6\n }, {\n \"id\": \"2\",\n \"displayName\": \"dog\",\n \"timeSegmentStart\": \"1.2s\",\n \"timeSegmentEnd\": \"3.4s\",\n \"frames\": [{\n \"timeOffset\": \"1.2s\",\n \"xMin\": 0.1,\n \"xMax\": 0.2,\n \"yMin\": 0.3,\n \"yMax\": 0.4\n }, {\n \"timeOffset\": \"3.4s\",\n \"xMin\": 0.2,\n \"xMax\": 0.3,\n \"yMin\": 0.4,\n \"yMax\": 0.5,\n }],\n \"confidence\": 0.5\n }]\n}\n```"]]
