Object detection
object_detection runs synchronous Ultralytics-compatible YOLO detection on a selected CPU or NVIDIA CUDA device.
Inputs
frame: a BGR NumPy image.
Outputs
detections: a list of dictionaries containing normalized, clippedbboxcoordinates,confidence,class_id, andclass_namewhen class names are available.
When to use
Use this operation for synchronous YOLO detection on CPU or CUDA. Use mx3_async_object_detection for a MemryX MX3 device.
Configuration
| Setting | Default | Notes |
|---|---|---|
model_id | required | Stable model ID selected from Model Library. Requires restart. |
device_id | cpu | Canonical cpu or cuda:N device ID. Requires restart. |
confidence_threshold | 0.25 | Minimum confidence, 0.0 to 1.0. |
iou_threshold | 0.45 | Non-maximum-suppression IoU threshold, 0.0 to 1.0. |
max_detections | 100 | Maximum results per frame, 1 to 1000. |
image_size | 0 | Square inference-size override. 0 uses model metadata. Requires restart. |
Important behavior and limitations
The operation supports detection models only. It does not support segmentation, pose, classification, fallback, or device load balancing.
Artifact selection is deterministic. CPU prefers ONNX, then PT. CUDA prefers TensorRT, then PT, then ONNX. CUDA ONNX must activate CUDAExecutionProvider on the selected device or initialization fails. TensorRT image-size overrides are rejected, and fixed-shape ONNX overrides must match the exported shape. Selecting mx3:N fails explicitly.