yolov9 icon indicating copy to clipboard operation
yolov9 copied to clipboard

export to ONNX with NMS

Open Egorundel opened this issue 4 months ago • 5 comments

Does the export to ONNX work with the NMS module? And dynamic batch size. If so, how do I do it?

As I understand it, the NMS module only works for TF models?

Egorundel avatar Feb 26 '24 08:02 Egorundel

https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004

WongKinYiu avatar Feb 26 '24 11:02 WongKinYiu

Why does an error occur when the onnx file generated by export.py is converted to trt_engine, and https://github.com/WongKinYiu/yolov9/issues/79#issue-2153547004 is normal.

zahidzqj avatar Feb 27 '24 03:02 zahidzqj

Why does an error occur when the onnx file generated by export.py is converted to trt_engine, and #79 (comment) is normal.

What tensorrt's version you are using?

huihui308 avatar Feb 28 '24 06:02 huihui308

Why does an error occur when the onnx file generated by export.py is converted to trt_engine, and #79 (comment) is normal.

What tensorrt's version you are using?

tensorrt==7.2.1.6

zahidzqj avatar Feb 28 '24 08:02 zahidzqj

Hello everyone!

I would like to introduce my open-source project - TensoRT-YOLO, a tool for deploying YOLO Series (Support YOLOv9) with Efficient NMS in TensorRT.

Key Features

  • Supports FLOAT32, FLOAT16 ONNX export, and TensorRT inference
  • Supports YOLOv5, YOLOv8, YOLOv9, PP-YOLOE, and PP-YOLOE+
  • Integrates EfficientNMS TensorRT plugin for accelerated post-processing
  • Utilizes CUDA kernel functions to accelerate preprocess
  • Supports C++ and Python inference

Perfomance

Perfomance Test using GPU RTX 2080Ti 22GB on AMD Ryzen 7 5700X 8-Core/ 128GB RAM.

Model Performance Evaluation using TensorRT engine using TensoRT-YOLO.

All models were deployed using FP16, BatchSize 4 and size 640.

YOLOv9 Series

This includes the YOLOv9-C, YOLOv9-E, YOLOv9-C-Converted, YOLOv9-E-Converted, GELAN-C and GELAN-E.

YOLOv9-C YOLOv9-E YOLOv9-C-Converted YOLOv9-E-Converted GELAN-C GELAN-E
Average Latency: 36.615ms Average Latency: 59.736ms Average Latency: 19.689ms Average Latency: 53.144ms Average Latency: 19.557ms Average Latency: 53.575ms

YOLOv8 Series

This includes the YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l and YOLOv8x.

YOLOv8n YOLOv8s YOLOv8m YOLOv8l YOLOv8x
Average Latency: 10.289ms Average Latency: 12.459ms Average Latency: 18.514ms Average Latency: 24.926ms Average Latency: 34.587ms

laugh12321 avatar Mar 03 '24 08:03 laugh12321