【语义分割 01】Open MMLab介绍
1 Tutorial
https://github.com/TommyZihao/MMSegmentation_Tutorials
https://github.com/TommyZihao/Train_Custom_Dataset
https://github.com/TommyZihao/aidlux_tutorial
OpenMMLab是一个由中国开发者主导的具有世界影响力的人工智能计算机视觉开源算法体系, 至今已经开源了30+视觉算法库。
OpenMMLab is a world-influential open source artificial intelligence computer vision algorithm system led by Chinese developers, and has opened 30+ visual algorithms library so far.
2 Introduce
openmmlab介绍视频：Github八万star，开源计算机视觉神器OpenMMLab_哔哩哔哩_bilibili
MMSegmentation分割：主要应用于医疗影像、遥感图像
MMSegmentation: Mainly applied to medical images and remote sensing images
MMPretrain：
MMHuman3D：
MMAction2：
MMagic：生成模型+底层视觉+AIGC算法库
MMagic: Generative model + low-level vision +AIGC algorithm library
3 Semantic segmentation annotation format
Integer mask
-
Watermelon data set
-
When to use semantic segmentation? When to split by instance?
如果不存在“实例”概念，只需要每个像素分类，用语义分割
If there is no "instance" concept, you just need to classify each pixel and use semantic segmentation.
如果需要目标检测预测框(计数、追踪、流量统计)用实例分割
If the target detection prediction box (counting, tracking, traffic statistics) is required, use the instance segmentation
Track-Anything：https://github.com/gaomingqi/Track-Anything
Segment-Anything：Segment Anything | Meta AI
-
Development trend of image segmentation.
Use Transformer architecture
Unified semantic segmentation, instance segmentation, panoramic segmentation
Vision Large Model (SAM)
Multimodal +AIGC(Text-generated picture, Picture-generated picture , Image editing)
-
Semantic segmentation evaluation
-
Summary
There are many application scenarios for semantic segmentation, and some time ago I also wanted to use sam on ROS, but it has not been made.
4 Install and configure the MMSegmentation environment
First，let's create a new virtual environment.
Install Pytorch using the following command.
pip3 install install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
Follow the second tutorial to make your environment successful!
