datvuthanh_hybridnets.md

layout	hub_detail
background-class	hub-background
body-class	hub
category	researchers
title	HybridNets
summary	HybridNets - End2End Perception Network
image	hybridnets.jpg
author	Dat Vu Thanh
tags	vision
github-link	https://github.com/datvuthanh/HybridNets
github-id	datvuthanh/HybridNets
featured_image_1	no-image
featured_image_2	no-image
accelerator	cuda-optional
demo-model-link	https://colab.research.google.com/drive/1Uc1ZPoPeh-lAhPQ1CloiVUsOIRAVOGWA

Before You Start

Start from a Python>=3.7 environment with PyTorch>=1.10 installed. To install PyTorch see https://pytorch.org/get-started/locally/. To install HybridNets dependencies:

pip install -qr https://raw.githubusercontent.com/datvuthanh/HybridNets/main/requirements.txt  # install dependencies

Model Description

HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection. HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.

Results

Traffic Object Detection

Model	Recall (%)	mAP@0.5 (%)
MultiNet	81.3	60.2
DLT-Net	89.4	68.4
Faster R-CNN	77.2	55.6
YOLOv5s	86.8	77.2
YOLOP	89.2	76.5
HybridNets	92.8	77.3

Drivable Area Segmentation

Model	Drivable mIoU (%)
MultiNet	71.6
DLT-Net	71.3
PSPNet	89.6
YOLOP	91.5
HybridNets	90.5

Lane Line Detection

Model	Accuracy (%)	Lane Line IoU (%)
Enet	34.12	14.64
SCNN	35.79	15.84
Enet-SAD	36.56	16.02
YOLOP	70.5	26.2
HybridNets	85.4	31.6

Load From PyTorch Hub

This example loads the pretrained HybridNets model and passes an image for inference.

import torch

# load model
model = torch.hub.load('datvuthanh/hybridnets', 'hybridnets', pretrained=True)

#inference
img = torch.randn(1,3,640,384)
features, regression, classification, anchors, segmentation = model(img)

Citation

If you find our paper and code useful for your research, please consider giving a star and citation:

@misc{vu2022hybridnets,
      title={HybridNets: End-to-End Perception Network}, 
      author={Dat Vu and Bao Ngo and Hung Phan},
      year={2022},
      eprint={2203.09035},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}