TensorRT Samples

Contents

Sample	Language	Format	Description
sampleAlgorithmSelector	C++	Caffe	Algorithm Selection API usage
sampleCharRNN	C++	INetwork	Building An RNN Network Layer By Layer
sampleDynamicReshape	C++	ONNX	Digit Recognition With Dynamic Shapes In TensorRT
sampleFasterRCNN	C++	Caffe	Object Detection With Faster R-CNN
sampleGoogleNet	C++	Caffe	Building And Running GoogleNet In TensorRT
sampleINT8	C++	Caffe	Performing Inference In INT8 Using Custom Calibration
sampleINT8API	C++	Caffe	Performing Inference In INT8 Precision
sampleMNIST	C++	Caffe	“Hello World” For TensorRT
sampleMNISTAPI	C++	INetwork	Building a Simple MNIST Network Layer by Layer
sampleNMT	C++	INetwork	Neural Machine Translation Using A seq2seq Model
sampleOnnxMNIST	C++	ONNX	“Hello World” For TensorRT With ONNX
sampleOnnxMnistCoordConvAC	C++	ONNX	Implementing CoordConv with a custom plugin
sampleIOFormats	C++	Caffe	Specifying TensorRT I/O Formats
sampleSSD	C++	Caffe	Object Detection With SSD
sampleUffFasterRCNN	C++	UFF	Object Detection With A TensorFlow FasterRCNN Network
sampleUffMNIST	C++	UFF	Import A TensorFlow Model And Run Inference
sampleUffMaskRCNN	C++	UFF	Object Detection And Instance Segmentation With MasK R-CNN Network
sampleUffPluginV2Ext	C++	UFF	Adding A Custom Layer That Supports INT8 I/O To Your Network
sampleUffSSD	C++	UFF	Object Detection With A TensorFlow SSD Network
trtexec	C++	All	TensorRT Command-Line Wrapper: trtexec
efficientdet	Python	ONNX	EfficientDet Object Detection with TensorRT
efficientnet	Python	ONNX	EfficientNet V1 and V2 Classification with TensorRT
end_to_end_tensorflow_mnist	Python	UFF	“Hello World” For TensorRT Using TensorFlow
engine_refit_mnist	Python	INetwork	Refitting A TensorRT Engine
int8_caffe_mnist	Python	Caffe	INT8 Calibration
introductory_parser_samples	Python	Any	Introduction To Importing Models Using TensorRT Parsers
network_api_pytorch_mnist	Python	INetwork	“Hello World” For TensorRT
onnx_packnet	Python	ONNX	TensorRT Inference Of ONNX Models With Custom Layers
tensorflow_object_detection_api	Python	ONNX	TensorFlow Object Detection API Models in TensorRT
uff_custom_plugin	Python	INetwork	Adding A Custom Layer To Your TensorFlow Network In TensorRT
uff_ssd	Python	UFF	Object Detection with SSD
yolov3_onnx	Python	ONNX	Object Detection Using YOLOv3 With TensorRT ONNX Backend

Known Limitations

• UFF converter and GraphSurgeon tools are only supported with Tensorflow 1.x
• For the UFF samples, please use the NVIDIA tf1 (Tensorflow 1.x) for running these tests or install Tensorflow 1.x manually.