tensorrt-conversion

Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

InsightFace REST API for easy deployment of face recognition services with TensorRT in Docker.

针对pytorch模型的自动化模型结构分析和修改工具集，包含自动分析模型结构的模型压缩算法库

Yolov5 TensorRT Implementations

this is a tensorrt version unet, inspired by tensorrtx

Advanced inference pipeline using NVIDIA Triton Inference Server for CRAFT Text detection (Pytorch), included converter from Pytorch -> ONNX -> TensorRT, Inference pipelines (TensorRT, Triton server - multi-format). Supported model format for Triton inference: TensorRT engine, Torchscript, ONNX

Using TensorRT for Inference Model Deployment.

Simple tool for PyTorch >> ONNX >> TensorRT conversion

Tools for Nvidia Jetson Nano, TX2, Xavier.

Advance inference performance using TensorRT for CRAFT Text detection. Implemented modules to convert Pytorch -> ONNX -> TensorRT, with dynamic shapes (multi-size input) inference.

Convenient Convert CRAFT Text detection pretrain Pytorch model into TensorRT engine directly, without ONNX step between

Dockerized TensorRT inference engine with ONNX model conversion tool and ResNet50 preprocess and postprocess C++ implementation

TensorRT optimises any Deep Learning model by not only making it lightweight but also by accelerating its inference speed with an idea to extract every ounce of performance from the model, making it perfect to be deployed at the edge. This repository helps you convert any Deep Learning model from TensorFlow to TensorRT!

Export (from Onnx) and Inference TensorRT engine with Python

Jetson TX2 compatible TensorFlow's ssd_mobilenet_v2_coco for TensorRT 6 / JetPack 4.3

This project is a notebook of learning TensorRT.

Convert popular Deep learning models to TensorRT using C++ API (preferably)

TensorRT implementation with Tensorflow 2

Experimenting with Cifar-10 dataset to understand and implement various Deep Learning Techniques and CNN Architectures for Image Classification.