Jetson Inference: imageNet - Jetson Inference

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DNN Vision Library |

imageNet DNN Vision Library (jetson-inference)

Image recognition DNN (GoogleNet, AlexNet, ResNet) More...

Classes

Macros

Detailed Description

Image recognition DNN (GoogleNet, AlexNet, ResNet)

Class Documentation

◆imageNet

| class imageNet |

Image recognition with classification networks, using TensorRT.

Inheritance diagram for imageNet:

Public Types

| | typedef std::vector< std::pair< uint32_t, float > > | Classifications | | | List of classification results where each entry represents a (classID, confidence) pair. More...
| | | |

Public Member Functions

| | virtual | ~imageNet () | | | Destroy. More...
| | | | template<typename T > | | int | Classify (T *image, uint32_t width, uint32_t height, float *confidence=NULL) | | | Predict the maximum-likelihood image class whose confidence meets the minimum threshold. More...
| | | | int | Classify (void *image, uint32_t width, uint32_t height, imageFormat format, float *confidence=NULL) | | | Predict the maximum-likelihood image class whose confidence meets the minimum threshold. More...
| | | | int | Classify (float *rgba, uint32_t width, uint32_t height, float *confidence=NULL, imageFormat format=IMAGE_RGBA32F) | | | Predict the maximum-likelihood image class whose confidence meets the minimum threshold. More...
| | | | template<typename T > | | int | Classify (T *image, uint32_t width, uint32_t height, Classifications &classifications, int topK=1) | | | Classify the image and return the topK image classification results that meet the minimum confidence threshold set by SetThreshold() or the --threshold command-line argument. More...
| | | | int | Classify (void *image, uint32_t width, uint32_t height, imageFormat format, Classifications &classifications, int topK=1) | | | Classify the image and return the topK image classification results that meet the minimum confidence threshold set by SetThreshold() or the --threshold command-line argument. More...
| | | | uint32_t | GetNumClasses () const | | | Retrieve the number of image recognition classes (typically 1000) More...
| | | | const char * | GetClassLabel (int index) const | | | Retrieve the description of a particular class. More...
| | | | const char * | GetClassDesc (int index) const | | | Retrieve the description of a particular class. More...
| | | | const char * | GetClassSynset (int index) const | | | Retrieve the class synset category of a particular class. More...
| | | | const char * | GetClassPath () const | | | Retrieve the path to the file containing the class descriptions. More...
| | | | float | GetThreshold () const | | | Return the confidence threshold used for classification. More...
| | | | void | SetThreshold (float threshold) | | | Set the confidence threshold used for classification. More...
| | | | float | GetSmoothing () const | | | Return the temporal smoothing weight or number of frames in the smoothing window. More...
| | | | void | SetSmoothing (float factor) | | | Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average). More...
| | | | Public Member Functions inherited from tensorNet | | virtual | ~tensorNet () | | | Destory. More...
| | | | bool | LoadNetwork (const char *prototxt, const char *model, const char *mean=NULL, const char *input_blob="data", const char *output_blob="prob", uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL) | | | Load a new network instance. More...
| | | | bool | LoadNetwork (const char *prototxt, const char *model, const char *mean, const char *input_blob, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL) | | | Load a new network instance with multiple output layers. More...
| | | | bool | LoadNetwork (const char *prototxt, const char *model, const char *mean, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL) | | | Load a new network instance with multiple input layers. More...
| | | | bool | LoadNetwork (const char *prototxt, const char *model, const char *mean, const char *input_blob, const Dims3 &input_dims, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL) | | | Load a new network instance (this variant is used for UFF models) More...
| | | | bool | LoadNetwork (const char *prototxt, const char *model, const char *mean, const std::vector< std::string > &input_blobs, const std::vector< Dims3 > &input_dims, const std::vector< std::string > &output_blobs, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true, nvinfer1::IInt8Calibrator *calibrator=NULL, cudaStream_t stream=NULL) | | | Load a new network instance with multiple input layers (used for UFF models) More...
| | | | bool | LoadEngine (const char *engine_filename, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL) | | | Load a network instance from a serialized engine plan file. More...
| | | | bool | LoadEngine (char *engine_stream, size_t engine_size, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, nvinfer1::IPluginFactory *pluginFactory=NULL, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL) | | | Load a network instance from a serialized engine plan file. More...
| | | | bool | LoadEngine (nvinfer1::ICudaEngine *engine, const std::vector< std::string > &input_blobs, const std::vector< std::string > &output_blobs, deviceType device=DEVICE_GPU, cudaStream_t stream=NULL) | | | Load network resources from an existing TensorRT engine instance. More...
| | | | bool | LoadEngine (const char *filename, char **stream, size_t *size) | | | Load a serialized engine plan file into memory. More...
| | | | void | EnableLayerProfiler () | | | Manually enable layer profiling times. More...
| | | | void | EnableDebug () | | | Manually enable debug messages and synchronization. More...
| | | | bool | AllowGPUFallback () const | | | Return true if GPU fallback is enabled. More...
| | | | deviceType | GetDevice () const | | | Retrieve the device being used for execution. More...
| | | | precisionType | GetPrecision () const | | | Retrieve the type of precision being used. More...
| | | | bool | IsPrecision (precisionType type) const | | | Check if a particular precision is being used. More...
| | | | cudaStream_t | GetStream () const | | | Retrieve the stream that the device is operating on. More...
| | | | cudaStream_t | CreateStream (bool nonBlocking=true) | | | Create and use a new stream for execution. More...
| | | | void | SetStream (cudaStream_t stream) | | | Set the stream that the device is operating on. More...
| | | | const char * | GetPrototxtPath () const | | | Retrieve the path to the network prototxt file. More...
| | | | const char * | GetModelPath () const | | | Retrieve the full path to model file, including the filename. More...
| | | | const char * | GetModelFilename () const | | | Retrieve the filename of the file, excluding the directory. More...
| | | | modelType | GetModelType () const | | | Retrieve the format of the network model. More...
| | | | bool | IsModelType (modelType type) const | | | Return true if the model is of the specified format. More...
| | | | uint32_t | GetInputLayers () const | | | Retrieve the number of input layers to the network. More...
| | | | uint32_t | GetOutputLayers () const | | | Retrieve the number of output layers to the network. More...
| | | | Dims3 | GetInputDims (uint32_t layer=0) const | | | Retrieve the dimensions of network input layer. More...
| | | | uint32_t | GetInputWidth (uint32_t layer=0) const | | | Retrieve the width of network input layer. More...
| | | | uint32_t | GetInputHeight (uint32_t layer=0) const | | | Retrieve the height of network input layer. More...
| | | | uint32_t | GetInputSize (uint32_t layer=0) const | | | Retrieve the size (in bytes) of network input layer. More...
| | | | float * | GetInputPtr (uint32_t layer=0) const | | | Get the CUDA pointer to the input layer's memory. More...
| | | | Dims3 | GetOutputDims (uint32_t layer=0) const | | | Retrieve the dimensions of network output layer. More...
| | | | uint32_t | GetOutputWidth (uint32_t layer=0) const | | | Retrieve the width of network output layer. More...
| | | | uint32_t | GetOutputHeight (uint32_t layer=0) const | | | Retrieve the height of network output layer. More...
| | | | uint32_t | GetOutputSize (uint32_t layer=0) const | | | Retrieve the size (in bytes) of network output layer. More...
| | | | float * | GetOutputPtr (uint32_t layer=0) const | | | Get the CUDA pointer to the output memory. More...
| | | | float | GetNetworkFPS () | | | Retrieve the network frames per second (FPS). More...
| | | | float | GetNetworkTime () | | | Retrieve the network runtime (in milliseconds). More...
| | | | const char * | GetNetworkName () const | | | Retrieve the network name (it's filename). More...
| | | | float2 | GetProfilerTime (profilerQuery query) | | | Retrieve the profiler runtime (in milliseconds). More...
| | | | float | GetProfilerTime (profilerQuery query, profilerDevice device) | | | Retrieve the profiler runtime (in milliseconds). More...
| | | | void | PrintProfilerTimes () | | | Print the profiler times (in millseconds). More...
| | | |

Static Public Member Functions

| | static imageNet * | Create (const char *network="googlenet", uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true) | | | Load one of the following pre-trained models: More...
| | | | static imageNet * | Create (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_labels, const char *input=IMAGENET_DEFAULT_INPUT, const char *output=IMAGENET_DEFAULT_OUTPUT, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true) | | | Load a new network instance. More...
| | | | static imageNet * | Create (int argc, char **argv) | | | Load a new network instance by parsing the command line. More...
| | | | static imageNet * | Create (const commandLine &cmdLine) | | | Load a new network instance by parsing the command line. More...
| | | | static const char * | Usage () | | | Usage string for command line arguments to Create()More...
| | | | Static Public Member Functions inherited from tensorNet | | static bool | LoadClassLabels (const char *filename, std::vector< std::string > &descriptions, int expectedClasses=-1) | | | Load class descriptions from a label file. More...
| | | | static bool | LoadClassLabels (const char *filename, std::vector< std::string > &descriptions, std::vector< std::string > &synsets, int expectedClasses=-1) | | | Load class descriptions and synset strings from a label file. More...
| | | | static bool | LoadClassColors (const char *filename, float4 *colors, int expectedClasses, float defaultAlpha=255.0f) | | | Load class colors from a text file. More...
| | | | static bool | LoadClassColors (const char *filename, float4 **colors, int expectedClasses, float defaultAlpha=255.0f) | | | Load class colors from a text file. More...
| | | | static float4 | GenerateColor (uint32_t classID, float alpha=255.0f) | | | Procedurally generate a color for a given class index with the specified alpha value. More...
| | | | static precisionType | SelectPrecision (precisionType precision, deviceType device=DEVICE_GPU, bool allowInt8=true) | | | Resolve a desired precision to a specific one that's available. More...
| | | | static precisionType | FindFastestPrecision (deviceType device=DEVICE_GPU, bool allowInt8=true) | | | Determine the fastest native precision on a device. More...
| | | | static std::vector< precisionType > | DetectNativePrecisions (deviceType device=DEVICE_GPU) | | | Detect the precisions supported natively on a device. More...
| | | | static bool | DetectNativePrecision (const std::vector< precisionType > &nativeTypes, precisionType type) | | | Detect if a particular precision is supported natively. More...
| | | | static bool | DetectNativePrecision (precisionType precision, deviceType device=DEVICE_GPU) | | | Detect if a particular precision is supported natively. More...
| | | |

Protected Member Functions

| | | imageNet () | | | | bool | init (const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_path, const char *input, const char *output, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback) | | | | bool | loadClassInfo (const char *filename, int expectedClasses=-1) | | | | bool | preProcess (void *image, uint32_t width, uint32_t height, imageFormat format) | | | | float * | applySmoothing () | | | | Protected Member Functions inherited from tensorNet | | | tensorNet () | | | Constructor. More...
| | | | bool | ProcessNetwork (bool sync=true) | | | Execute processing of the network. More...
| | | | bool | ProfileModel (const std::string &deployFile, const std::string &modelFile, const std::vector< std::string > &inputs, const std::vector< Dims3 > &inputDims, const std::vector< std::string > &outputs, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator, char **engineStream, size_t *engineSize) | | | Create and output an optimized network model. More...
| | | | bool | ConfigureBuilder (nvinfer1::IBuilder *builder, uint32_t maxBatchSize, uint32_t workspaceSize, precisionType precision, deviceType device, bool allowGPUFallback, nvinfer1::IInt8Calibrator *calibrator) | | | Configure builder options. More...
| | | | bool | ValidateEngine (const char *model_path, const char *cache_path, const char *checksum_path) | | | Validate that the model already has a built TensorRT engine that exists and doesn't need updating. More...
| | | | void | PROFILER_BEGIN (profilerQuery query) | | | Begin a profiling query, before network is run. More...
| | | | void | PROFILER_END (profilerQuery query) | | | End a profiling query, after the network is run. More...
| | | | bool | PROFILER_QUERY (profilerQuery query) | | | Query the CUDA part of a profiler query. More...
| | | |

Protected Attributes

Member Typedef Documentation

◆Classifications

| typedef std::vector<std::pair<uint32_t, float> > imageNet::Classifications |

List of classification results where each entry represents a (classID, confidence) pair.

Constructor & Destructor Documentation

◆~imageNet()

| virtual imageNet::~imageNet | ( | | ) | |

| virtual |

Destroy.

◆imageNet()

| imageNet::imageNet | ( | | ) | |

| protected |

Member Function Documentation

◆applySmoothing()

| float* imageNet::applySmoothing | ( | | ) | |

| protected |

◆Classify() [1/5]

Predict the maximum-likelihood image class whose confidence meets the minimum threshold.

Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by SetThreshold() or the --threshold command-line argument.

Parameters

ReturnsID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned. **Deprecated:**this overload of Classify() provides legacy compatibility with float* type (RGBA32F).

◆Classify() [2/5]

template<typename T >

| inline |

Classify the image and return the topK image classification results that meet the minimum confidence threshold set by SetThreshold() or the --threshold command-line argument.

Parameters

| image | input image in CUDA device memory. | | width | width of the input image in pixels. | | height | height of the input image in pixels. | | classifications | returns a list of the topK (classID, confidence) classification resuts, sorted from highest to lowest confidence. | | topK | the number of predictions to return (it can be less than this number if there weren't that many valid predictions) The default value of topK is 1, in which case only the highest-confidence result wil be returned. If the value of topK is <= 0, then all the valid predictions with confidence >= threshold will be returned. |

ReturnsID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned.

◆Classify() [3/5]

template<typename T >

| inline |

Predict the maximum-likelihood image class whose confidence meets the minimum threshold.

Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by SetThreshold() or the --threshold command-line argument.

Parameters

ReturnsID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned.

◆Classify() [4/5]

Classify the image and return the topK image classification results that meet the minimum confidence threshold set by SetThreshold() or the --threshold command-line argument.

Parameters

| image | input image in CUDA device memory. | | width | width of the input image in pixels. | | height | height of the input image in pixels. | | format | format of the image (rgb8, rgba8, rgb32f, rgba32f are supported) | | classifications | returns a list of the topK (classID, confidence) classification resuts, sorted from highest to lowest confidence. | | topK | the number of predictions to return (it can be less than this number if there weren't that many valid predictions) The default value of topK is 1, in which case only the highest-confidence result wil be returned. If the value of topK is <= 0, then all the valid predictions with confidence >= threshold will be returned. |

ReturnsID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned.

◆Classify() [5/5]

Predict the maximum-likelihood image class whose confidence meets the minimum threshold.

Either the class with the maximum probability will be returned, or -1 if no class meets the threshold set by SetThreshold() or the --threshold command-line argument.

Parameters

ReturnsID of the class with the highest confidence, or -1 if no classes met the threshold. If a runtime error occurred during processing, then a value of -2 will be returned.

◆Create() [1/4]

| static |

Load one of the following pre-trained models:

alexnet, googlenet, googlenet-12,
resnet-18, resnet-50, resnet-101, resnet-152,
vgg-16, vgg-19, inception-v4

These are all 1000-class models trained on ImageNet ILSVRC, except for googlenet-12 which is a 12-class subset of ILSVRC.

◆Create() [2/4]

| static |

Load a new network instance.

Parameters

◆Create() [3/4]

| static |

Load a new network instance by parsing the command line.

◆Create() [4/4]

| static imageNet* imageNet::Create | ( | int | argc, | | | | char ** | argv | | | ) | | |

| static |

Load a new network instance by parsing the command line.

◆GetClassDesc()

| const char* imageNet::GetClassDesc | ( | int | index | ) | const |

| inline |

Retrieve the description of a particular class.

◆GetClassLabel()

| const char* imageNet::GetClassLabel | ( | int | index | ) | const |

| inline |

Retrieve the description of a particular class.

◆GetClassPath()

| const char* imageNet::GetClassPath | ( | | ) | const |

| inline |

Retrieve the path to the file containing the class descriptions.

◆GetClassSynset()

| const char* imageNet::GetClassSynset | ( | int | index | ) | const |

| inline |

Retrieve the class synset category of a particular class.

◆GetNumClasses()

| uint32_t imageNet::GetNumClasses | ( | | ) | const |

| inline |

Retrieve the number of image recognition classes (typically 1000)

◆GetSmoothing()

| float imageNet::GetSmoothing | ( | | ) | const |

| inline |

Return the temporal smoothing weight or number of frames in the smoothing window.

◆GetThreshold()

| float imageNet::GetThreshold | ( | | ) | const |

| inline |

Return the confidence threshold used for classification.

◆init()

| protected |

◆loadClassInfo()

| bool imageNet::loadClassInfo | ( | const char * | filename, | | | | int | expectedClasses = -1 | | | ) | | |

| protected |

◆preProcess()

| protected |

◆SetSmoothing()

| inline |

Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average).

This filters the confidence values of each class over ~N frames to reduce noise and jitter. In lieu of storing a history of past data, this uses an accumulated approximation of EMA:

EMA(x,t) = EMA(x, t-1) + w * (x - EMA(x, t-1))

where x is a class softmax output logit, t is the timestep, and w is the smoothing weight.

Parameters

| factor | either a weight between [0,1] that's placed on the latest confidence values, or the smoothing window as a number of frames (where the weight will be 1/N).
For example, a factor of N=5 would average over approximately the last 5 frames, and would be equivalent to specifying a weight of 0.2 (either can be used). A weight closer to 1 will be more responsive to changes, but also more noisy.
Setting this to 0 or 1 will disable smoothing and use the unfiltered outputs. |

Notethis can also be set using the --smoothing=N command-line argument.

◆SetThreshold()

| inline |

Set the confidence threshold used for classification.

Classes with a confidence below this threshold will be ignored.

Notethis can also be set using the --threshold=N command-line argument.

◆Usage()

| static const char* imageNet::Usage | ( | | ) | |

| inlinestatic |

Usage string for command line arguments to Create()

Member Data Documentation

◆mClassDesc

| std::vector<std::string> imageNet::mClassDesc |

| protected |

◆mClassPath

| std::string imageNet::mClassPath |

| protected |

◆mClassSynset

| std::vector<std::string> imageNet::mClassSynset |

| protected |

◆mNumClasses

| uint32_t imageNet::mNumClasses |

| protected |

◆mSmoothingBuffer

| float* imageNet::mSmoothingBuffer |

| protected |

◆mSmoothingFactor

| float imageNet::mSmoothingFactor |

| protected |

◆mThreshold

| float imageNet::mThreshold |

| protected |

Macro Definition Documentation

◆IMAGENET_DEFAULT_INPUT

| #define IMAGENET_DEFAULT_INPUT "data" |

Name of default input blob for imageNet model.

◆IMAGENET_DEFAULT_OUTPUT

| #define IMAGENET_DEFAULT_OUTPUT "prob" |

Name of default output confidence values for imageNet model.

◆IMAGENET_DEFAULT_THRESHOLD

| #define IMAGENET_DEFAULT_THRESHOLD 0.01f |

Default value of the minimum confidence threshold for classification.

◆IMAGENET_MODEL_TYPE

| #define IMAGENET_MODEL_TYPE "classification" |

The model type for imageNet in data/networks/models.json.

◆IMAGENET_USAGE_STRING

| #define IMAGENET_USAGE_STRING |

Value:

"imageNet arguments: \n" \

" --network=NETWORK pre-trained model to load, one of the following:\n" \

" * alexnet\n" \

" * googlenet (default)\n" \

" * googlenet-12\n" \

" * resnet-18\n" \

" * resnet-50\n" \

" * resnet-101\n" \

" * resnet-152\n" \

" * vgg-16\n" \

" * vgg-19\n" \

" * inception-v4\n" \

" --model=MODEL path to custom model to load (caffemodel, uff, or onnx)\n" \

" --prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)\n" \

" --labels=LABELS path to text file containing the labels for each class\n" \

" --input-blob=INPUT name of the input layer (default is '" IMAGENET_DEFAULT_INPUT "')\n" \

" --output-blob=OUTPUT name of the output layer (default is '" IMAGENET_DEFAULT_OUTPUT "')\n" \

" --threshold=CONF minimum confidence threshold for classification (default is 0.01)\n" \

" --smoothing=WEIGHT weight between [0,1] or number of frames (disabled by default)\n" \

" --profile enable layer profiling in TensorRT\n\n"

Standard command-line options able to be passed to imageNet::Create()

◆POSENET_USAGE_STRING

| #define POSENET_USAGE_STRING |

Value:

"poseNet arguments: \n" \

" --network=NETWORK pre-trained model to load, one of the following:\n" \

" * resnet18-body (default)\n" \

" * resnet18-hand\n" \

" * densenet121-body\n" \

" --model=MODEL path to custom model to load (caffemodel, uff, or onnx)\n" \

" --prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)\n" \

" --labels=LABELS path to text file containing the labels for each class\n" \

" --input-blob=INPUT name of the input layer (default is '" POSENET_DEFAULT_INPUT "')\n" \

" --output-cvg=COVERAGE name of the coverge output layer (default is '" POSENET_DEFAULT_CMAP "')\n" \

" --output-bbox=BOXES name of the bounding output layer (default is '" POSENET_DEFAULT_PAF "')\n" \

" --mean-pixel=PIXEL mean pixel value to subtract from input (default is 0.0)\n" \

" --threshold=THRESHOLD minimum threshold for detection (default is 0.5)\n" \

" --overlay=OVERLAY detection overlay flags (e.g. --overlay=links,keypoints)\n" \

" valid combinations are: 'box', 'links', 'keypoints', 'none'\n" \

" --keypoint-scale=X radius scale for keypoints, relative to image (default: 0.0052)\n" \

" --link-scale=X line width scale for links, relative to image (default: 0.0013)\n" \

" --profile enable layer profiling in TensorRT\n\n"

Standard command-line options able to be passed to poseNet::Create()

IMAGENET_DEFAULT_INPUT

#define IMAGENET_DEFAULT_INPUT

Name of default input blob for imageNet model.

Definition: imageNet.h:34

IMAGENET_DEFAULT_OUTPUT

#define IMAGENET_DEFAULT_OUTPUT

Name of default output confidence values for imageNet model.

Definition: imageNet.h:40

POSENET_DEFAULT_PAF

#define POSENET_DEFAULT_PAF

Name of default output blob of the Part Affinity Field (PAF) for pose estimation ONNX model.

Definition: poseNet.h:49

POSENET_DEFAULT_INPUT

#define POSENET_DEFAULT_INPUT

Name of default input blob for pose estimation ONNX model.

Definition: poseNet.h:37

POSENET_DEFAULT_CMAP

#define POSENET_DEFAULT_CMAP

Name of default output blob of the confidence map for pose estimation ONNX model.

Definition: poseNet.h:43

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