YOLOv3-Torch2TRT

Introduction

Convert YOLOv3 and YOLOv3-tiny (PyTorch version) into TensorRT models, through the torch2trt Python API.

Installation

Clone the repo

git clone https://github.com/DocF/YOLOv3-Torch2TRT.git

Download pretrained weights

$ cd weights/
$ bash download_weights.sh

Requirements

Two special Python packages are needed:

• tensorrt

• torch2trt

Due to the upsampling operation in YOLO, according to torch2trt API introduction, you need to install the version with plugins.

Installation reference: https://github.com/NVIDIA-AI-IOT/torch2trt

Check torch2trt API

python3 check.py

Inference Acceleration

Acceleration Techs：

• FP16
• TensorRT

Here are some results on TITAN xp:

Model name	Input Size	FP16	Entire Mode*(FPS)	Backbone+FeatureNet(FPS)
YOLOv3	320×320		87.58 Hz	102.95 Hz
	320×320	✔️	83.63 Hz	100.36 Hz
YOLOv3-TRT	320×320		110.74 Hz	121.81 Hz
	320×320	✔️	106.92 Hz	124.95 Hz
YOLOv3-tiny	320×320		354.10 Hz	668.71 Hz
	320×320	✔️	379.11 Hz	727.82 Hz
YOLOv3-tiny-TRT	320×320		684.75 Hz	1035.11 Hz
	320×320	✔️	649.71 Hz	1012.66 Hz

Entire Model* = Backbone + Feature Net + YOLO Head

python3 detect.py

Statement

This repo is based on PyTorch-YOLOv3. Thx for the great repo.