SegNet including indices pooling for Semantic Segmentation with tensorflow and keras

Last update: Dec 23, 2022

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Overview

SegNet

SegNet is a model of semantic segmentation based on Fully Comvolutional Network.

This repository contains the implementation of learning and testing in keras and tensorflow. Also included is a custom layer implementation of index pooling, a new property of segnet.

architecture

encoder decoder architecture
fully convolutional network
indices pooling

description

This repository is SegNet architecture for Semantic Segmentation. The repository of other people's segmentation, pooling with indices not implemented.But In this repository we implemented pooling layer and unpooling layer with indices at MyLayers.py.

Segnet architecture is early Semantic Segmentation model,so acccuracy is low but fast. In the future, we plan to implement models with high accuracy.(UNet,PSPNet,Pix2Pix ect..)

Usage

train

python SegNet.py [--options your dataset]

Owner

Yuta Kamikawa

GitHub Repository

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SegNet including indices pooling for Semantic Segmentation with tensorflow and keras

Related tags

Overview

SegNet

architecture

description

Usage

train

Owner

Yuta Kamikawa

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

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