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fragan's Introduction

FRAGAN

A Feature Refinement and Adaptive Generative Adversarial Network for Thermal Infrared Image Colorization

Prerequisites

  • python 3.7
  • torch 1.13.1
  • torchvision 0.14.1
  • dominate
  • visdom

Pretrianed

We provide the pretrained model: Google

Trian

python train.py --dataroot [dataset root] --name [experiment_name] --phase train --which_epoch latest

Test

python test.py --dataroot [dataset root] --name [experiment_name] --phase test --which_epoch latest

Colorization results

KAIST dataset

KAIST

FLIR dataset

FLIR

Note

We only uploaded the paired strategy, the remaining strategies can be found in CycleGAN and CUT.

Acknowledgments

This code heavily borrowes from MUGAN.

fragan's People

Contributors

cyanymore avatar

Stargazers

Ting Li avatar Danee avatar  avatar

Watchers

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fragan's Issues

关于测试结果(生成的可见光图像)

您好,我测试了flir测试集的部分样本,测试结果出现了flir_id_fake_B, flir_id_real_A即flir_id_real_B。感觉是real_A和real_B是一张图像的两个区域,生成的fake_B又是对应的real_A。我不明白为什么是这样的结果,不应该是对一张红外图像生成对应的可见光图像嘛?
期待您的回复呢,感谢!

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