AI practical training camp & image segmentation MMSegmentation

An introduction

MMSegmentation is an open source toolbox for semantic segmentation based on PyTorch. It is part of the OpenMMLab project.

Two main features

Unified benchmark platform

We integrate various semantic segmentation algorithms into a unified toolbox for benchmarking.

Modular design

• MMSegmentation decouples the segmentation framework into different module components. By combining different module components, users can easily build customized segmentation models.
• Rich plug-and-play algorithms and models
• MMSegmentation supports many mainstream and latest detection algorithms, such as PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
• high speed
• Training speed is faster or comparable to other semantic segmentation code libraries.

Introduction to three-divided tasks

The segmentation task refers to the task of segmenting an image into several regions. Each region usually corresponds to an object or a part of an object in the image. Segmentation tasks are usually divided into two categories: semantic segmentation and instance segmentation.

• Semantic segmentation is to assign a semantic label to each pixel in an image. The label indicates which category the pixel belongs to, such as people, cars, trees, sky, etc. The goal of semantic segmentation is to distinguish different objects or parts of objects in an image. Commonly used methods include pixel-based classification methods and region-based methods.
• Instance segmentation is to segment each object instance in an image, usually by segmenting each object in the image to obtain the object's bounding box and mask information. The goal of instance segmentation is to segment each object in the image and obtain independent object instances. Commonly used methods include bounding box-based methods, mask-based methods, and hybrid methods based on bounding boxes and masks.

The segmentation task is an important task in the field of computer vision and is widely used in fields such as autonomous driving, medical image analysis, geographic information systems, and intelligent monitoring. At present, deep learning technology has achieved good results in segmentation tasks and continues to promote the development of segmentation tasks.