Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
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Updated
Jun 4, 2024 - C++
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
Using TensorRT for Inference Model Deployment.
VitPose without MMCV dependencies
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
Convert yolo models to ONNX, TensorRT add NMSBatched.
The real-time Instance Segmentation Algorithm SparseInst running on TensoRT and ONNX
Advance inference performance using TensorRT for CRAFT Text detection. Implemented modules to convert Pytorch -> ONNX -> TensorRT, with dynamic shapes (multi-size input) inference.
Tools for Nvidia Jetson Nano, TX2, Xavier.
Convenient Convert CRAFT Text detection pretrain Pytorch model into TensorRT engine directly, without ONNX step between
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!
Simple tool for PyTorch >> ONNX >> TensorRT conversion
Export (from Onnx) and Inference TensorRT engine with Python
Base on tensorrt version 8.2.4, compare inference speed for different tensorrt api.
Jetson TX2 compatible TensorFlow's ssd_mobilenet_v2_coco for TensorRT 6 / JetPack 4.3
A CLI tool to convert Keras models to ONNX models and TensorRT engines
Convert popular Deep learning models to TensorRT using C++ API (preferably)
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