# TensorRT_yolo3

**Repository Path**: dcctt/TensorRT_yolo3

## Basic Information

- **Project Name**: TensorRT_yolo3
- **Description**: use TensorRT accelerate yolo3
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-01-03
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Update on 2019-04-19
- I have optimized and upgraded this project. So：
- If you see this project for the first time, you can jump to [This project](https://github.com/Cw-zero/TensorRT_yolo3_module) directly. 
- If you meet some bug on this project,you can try [This project](https://github.com/Cw-zero/TensorRT_yolo3_module).

# Use TensorRT accelerate yolo3
---
## 1. How to run this project
- a. Download yolo3.weight from [this](https://pjreddie.com/media/files/yolov3.weights), 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](https://github.com/ayooshkathuria/pytorch-yolo-v3), 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.