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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.