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Classification

The Classifier task accepts any ONNX classifier with an output shape of (1, num_classes) (torchvision-style). It handles preprocessing, normalization, optional softmax, and label resolution for you, returning a ClassificationResults envelope.

Building the classifier

from ort_vision_sdk import Classifier

clf = Classifier(
    "resnet50.onnx",
    labels="imagenet_labels.txt",   # see "Labels" below
    input_size=(224, 224),          # default (width, height)
    mean=(0.485, 0.456, 0.406),     # ImageNet mean by default
    std=(0.229, 0.224, 0.225),      # ImageNet std by default
    apply_softmax=True,             # False if your model already outputs probs
)

Additional parameters: providers (execution-provider list) and session_options (an ort.SessionOptions).

import { Classifier } from "@mauriciobenjamin700/ort-vision-sdk-web";

const clf = await Classifier.create("/models/resnet50.onnx", {
  labels: ["tench", "goldfish", /* ... */],  // or null + numClasses
  inputSize: [224, 224],                      // default
  applySoftmax: true,                         // default
  providers: ["webgpu", "wasm"],              // default order
});

When labels is null, pass numClasses so the SDK auto-generates class_0, class_1, ...

Predicting

predict() returns a length-1 list — use [0].

r = clf.predict("dog.jpg")[0]

print(r.cls, r.conf, r.name)   # top-1 (index, confidence, label)
print(r.probs.top1)            # top-1 index
print(r.probs.top5)            # array of the 5 most-probable indices
print(r.probs.top1conf)        # top-1 confidence
print(r.probs.top5conf)        # top-5 confidences
print(r.probabilities[:5])     # tuple of ClassProbability dataclasses

const r = (await clf.predict("/images/dog.jpg", { topK: 5 }))[0];

console.log(r.cls, r.conf, r.name);   // top-1
console.log(r.probs.top1, r.probs.top5);
console.log(r.probs.top1conf, r.probs.top5conf);
console.log(r.probabilities);          // ClassProbability[]

topK controls how many per-class probabilities are materialized in probabilities.

The Probs view

The bulk probs view mirrors Ultralytics' Probs interface: top1, top5, top1conf, top5conf, data. The ClassProbability dataclasses/objects carry the verbose fields (class_id/classId, class_name/className, probability) and expose Ultralytics-style aliases (cls, name).

Labels

See Labels in the Python guide (Python) and Labels in the Web guide (Web) — both platforms accept a preset ("coco"), a list/tuple, a sparse dict, a file path (Python), or null to auto-generate.

See also