BoundingBoxNetwork class
Neural network process object for bounding box detection.
Contents
- Reference
This class is a convenience class for a neural network which performs bounding box prediction.
Base classes
- class NeuralNetwork
- Neural network process object.
Public functions
- auto create(std::string modelFilename, float scaleFactor, float threshold, std::vector<std::vector<Vector2f>> anchors, BoundingBoxNetworkType type, float meanIntensity, float standardDeviationIntensity, std::vector<NeuralNetworkNode> inputNodes, std::vector<NeuralNetworkNode> outputNodes, std::string inferenceEngine, std::vector<std::string> customPlugins) -> std::shared_ptr<BoundingBoxNetwork>
- Create instance.
- void setThreshold(float threshold)
- void loadAttributes() override
- void setAnchors(std::vector<std::vector<Vector2f>> anchors)
- void setType(BoundingBoxNetworkType type)
Private functions
- void execute() override
Function documentation
std::shared_ptr<BoundingBoxNetwork> fast::
Create instance.
| Parameters | |
|---|---|
| modelFilename | Path to model to load |
| scaleFactor | A value which is multiplied with each pixel of input image before it is sent to the neural network. Use this to scale your pixels values. Default: 1.0 |
| threshold | Threshold for how high score a bounding box need to be accepted. |
| anchors | List of anchors to use when calculating bounding boxes from the output tensor. |
| type | Type of bounding box detection network. Used to determine how the output tensor should be handled to create bounding boxes. Default: YOLOv3 |
| meanIntensity | Mean intensity to subtract from each pixel of the input image |
| standardDeviationIntensity | Standard deviation to divide each pixel of the input image by |
| inputNodes | Specify names, and potentially shapes, of input nodes. Not necessary unless you only want to use certain inputs or specify the input shape manually. |
| outputNodes | Specify names, and potentially shapes, of output nodes to use. Not necessary unless you only want to use certain outputs or specify the output shape manually. |
| inferenceEngine | Specify which inference engine to use (TensorFlow, TensorRT, OpenVINO). By default, FAST will select the best inference engine available on your system. |
| customPlugins | Specify path to any custom plugins/operators to load |
| Returns | instance |