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Detection

The Detector task supports anchor-free YOLO heads (v8, v9, v10, v11, v12, v26). It letterboxes the image, runs the model, decodes the anchors, applies NMS, and maps the boxes back to the original-image pixels.

Building the detector

from ort_vision_sdk import Detector

det = Detector(
    "yolov8n.onnx",
    head="yolo",                # decoder family (default covers v8..v26)
    labels="coco",              # default — 80-class COCO preset
    input_size=(640, 640),      # default used for letterboxing
    conf_threshold=0.25,        # default minimum score
    iou_threshold=0.45,         # default NMS IoU
    max_detections=300,         # cap on detections per image
)

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

const det = await Detector.create("/models/yolov8n.onnx", {
  head: "yolo",                 // default
  labels: "coco",               // default
  inputSize: [640, 640],        // default
  confThreshold: 0.25,          // default
  iouThreshold: 0.45,           // default
});

Predicting

result = det.predict("street.jpg")[0]

The bulk Boxes view

The boxes view mirrors Ultralytics' Boxes interface:

print(result.boxes.xyxy)    # (N, 4) absolute pixels [x1, y1, x2, y2]
print(result.boxes.xywh)    # (N, 4) [cx, cy, w, h]
print(result.boxes.xyxyn)   # (N, 4) normalized
print(result.boxes.xywhn)   # (N, 4) normalized [cx, cy, w, h]
print(result.boxes.cls)     # (N,) int64
print(result.boxes.conf)    # (N,) float64
print(result.boxes.data)    # (N, 6) [x1, y1, x2, y2, conf, cls]

On Web, result.boxes exposes the same attributes.

Per-instance

for d in result:
    print(d.name, d.conf, d.box.xyxy)
    # d.cropped_image: HWC uint8 RGB ndarray of the box crop

for (const d of result) {
  console.log(d.className, d.confidence, d.bbox.asXyxy());
  // d.croppedImage: RGBImage of the box region
}

The Web BoundingBox exposes asXyxy() and asXywh().

Per-call overrides

You can override thresholds and filter classes on each predict():

result = det.predict(
    "img.jpg",
    conf_threshold=0.4,
    iou_threshold=0.5,
    classes=[0, 16],   # keep only these classes (e.g. person and dog)
)[0]

const result = (await det.predict("/img.jpg", {
  confThreshold: 0.4,
  iouThreshold: 0.5,
  classes: [0, 16],
}))[0];

Common patterns

Filter by class

people = [d for d in result if d.name == "person"]

Save crops

from PIL import Image
for i, d in enumerate(result):
    Image.fromarray(d.cropped_image).save(f"crop_{i}.png")

See also