A CLI tool to convert Keras models to ONNX models and TensorRT engines
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Updated
Dec 23, 2022 - Python
A CLI tool to convert Keras models to ONNX models and TensorRT engines
Jetson TX2 compatible TensorFlow's ssd_mobilenet_v2_coco for TensorRT 6 / JetPack 4.3
Convert popular Deep learning models to TensorRT using C++ API (preferably)
TensorRT implementation with Tensorflow 2
C++/C TensorRT Inference Example for models created with Pytorch/JAX/TF
Experimenting with Cifar-10 dataset to understand and implement various Deep Learning Techniques and CNN Architectures for Image Classification.
Based on TensorRT v8.2, build network for YOLOv5-v5.0 by myself, speed up YOLOv5-v5.0 inferencing
不同backend的模型转换与推理代码
Dockerized TensorRT inference engine with ONNX model conversion tool and ResNet50 and Ultraface preprocess and postprocess C++ implementation
This project is a notebook of learning TensorRT.
tensorrt-toy code
Based on tensorrt v8.0+, deploy detect, pose, segment of YOLOv8 with C++ and python api.
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
Convenient Convert CRAFT Text detection pretrain Pytorch model into TensorRT engine directly, without ONNX step between
Export (from Onnx) and Inference TensorRT engine with Python
Base on tensorrt version 8.2.4, compare inference speed for different tensorrt api.
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.
The real-time Instance Segmentation Algorithm SparseInst running on TensoRT and ONNX
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