Yolov--3--TensorRT中yolov3性能优化加速（基于caffe）

Yolov-1-TX2上用YOLOv3训练自己数据集的流程(VOC2007-TX2-GPU)

yolov-5-目标检测：YOLOv2算法原理详解

yolov--8--Tensorflow实现YOLO v3

yolov--9--YOLO v3的剪枝优化

yolov--10--目标检测模型的参数评估指标详解、概念解析

yolov--12--YOLOv3的原理深度剖析和关键点讲解

---

一、TX2中TensorRT的安装

https://blog.csdn.net/haoqimao_hard/article/details/81001844

https://github.com/dusty-nv/jetson-inference

平台：英伟达NVIDIA TX2开发板  环境：Ubuntu16.04， python2.7，cuda8.0，cudnn6.0.2.  OpenCV 2.4.13.1

caffe安装教程：

https://www.jianshu.com/p/e78c5c321248?tdsourcetag=s_pcqq_aiomsg

 

 

https://github.com/Cw-zero/TensorRT_yolo3

Use TensorRT accelerate yolo3

---

1. How to run this project

• a. Download yolo3.weight from this, and change the name to yolov3-608.weights.
• b. python yolov3_to_onnx.py, you will have a file named yolov3-608.onnx
• c. python onnx_to_tensorrt.py,you can get the result of detections.

2. Performance compare

• a.You can download and run this project, which our project is changed from it. It detection speed is about 100ms per image.

• b.Our project speed is about 62ms per image

3.Others

• If you are more familiar with Chinese, you can refer to this blog(https://www.cnblogs.com/justcoder/), which has more details.

 

https://github.com/ayooshkathuria/pytorch-yolo-v3