More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

Deploy DL/ ML inference pipelines with minimal extra code.

Set up CI in DL/ cuda/ cudnn/ TensorRT/ onnx2trt/ onnxruntime/ onnxsim/ Pytorch/ Triton-Inference-Server/ Bazel/ Tesseract/ PaddleOCR/ NVIDIA-docker/ minIO/ Supervisord on AGX or PC from scratch.

Provides an ensemble model to deploy a YoloV8 ONNX model to Triton

This repository utilizes the Triton Inference Server Client, which streamlines the complexity of model deployment.

The Purpose of this repository is to create a DeepStream/Triton-Server sample application that utilizes yolov7, yolov7-qat, yolov9 models to perform inference on video files or RTSP streams.

A template for delpoy AI server use django with tf_serving or triton_inference_serving

QuickStart for Deploying a Basic Model on the Triton Inference Server

Add Some plus extra features to transformers

Yolov8 onnx inference running on triton server