dnn_readNetFromONNX

Deep neural networksfunctionOpenCV 5.0.0

import { dnn_readNetFromONNX } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Neural-network inference →

Execute a trained model by passing tensors through its computation graph.

VISUAL WALKTHROUGHDeep learning

REFERENCE INPUTImage to classify

The sample drawing makes the data representation visible; it is not an actual model input/output pair.

→

RESULTInterpret the output tensor

These illustrative class scores show one possible output contract. Detection, segmentation and other networks require different interpretation.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

Reads a network model <a href="https://onnx.ai/">ONNX</a>.

dnn_readNetFromONNX(onnxFile: EmbindString, engine: number): dnn_Net;

2 available overloads

dnn_readNetFromONNX(onnxFile: EmbindString): dnn_Net;

dnn_readNetFromONNX(onnxFile: EmbindString, engine: number): dnn_Net;

onnxFile

path to the .onnx file with text description of the network architecture.

engine

select DNN engine to be used. With auto selection the new engine is used first and falls back to classic. Please pay attention that the new DNN does not support non-CPU back-ends for now.

Returns

Network object that ready to do forward, throw an exception in failure cases.

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.