YOLOP VS PaddleSeg

Compare YOLOP vs PaddleSeg and see what are their differences.

PaddleSeg

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc. (by PaddlePaddle)
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YOLOP PaddleSeg
5 17
1,822 8,253
3.1% 2.2%
3.2 7.4
6 months ago 11 days ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

YOLOP

Posts with mentions or reviews of YOLOP. We have used some of these posts to build our list of alternatives and similar projects.

PaddleSeg

Posts with mentions or reviews of PaddleSeg. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing YOLOP and PaddleSeg you can also consider the following projects:

Ultra-Fast-Lane-Detection - Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)

mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.

HybridNets - HybridNets: End-to-End Perception Network

jiant - jiant is an nlp toolkit

PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

SGDepth - [ECCV 2020] Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance

segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.

OneFormer - OneFormer: One Transformer to Rule Universal Image Segmentation, arxiv 2022 / CVPR 2023

HFT - [ICRA 2023] Official Pytorch implementation for HFT