Comments (6)
对的,simulation的时候要用CAVE 512×512,切出256×256的patch训练。real 训练的时候要用 CAVE 1024×1024,切出660×660的patch训练。在data prepare那里都有提供这两个数据集的下载链接。
from mst.
谢谢你的评论解决了我的困惑。尽管如此,我在随后的复现工作中又遇到了新的困难。我已经仔细查看了作者在真实实验中的可用代码,其中图像尺寸被裁切为了384,并且cave数据集中只取了30张图。我想知道代码中的默认设置是否不适合于660660的图像,或许我应该尝试将cave的图像选择由默认的30张更改为1024版本的205张?此外,如果是针对384384的图像所设计的代码,应该如何将观测图像采样至适合维度?
from mst.
我们训练的时候将图像裁成384是为了节省计算量。你可以完全采用我们的默认设定。训练张数就用30张即可。
当然你可以试一下把训练张数扩大到205张,只是那样比较费时间。
from mst.
对您的耐心表示感谢。我们试图采用您所推荐的第一种方法,即,将图像裁切至384,对模型进行训练。然而,这将导致一个问题,我需要在测试时对真实获取的观测图像进行裁切。换句话讲,我需要将660x714尺寸的观测图像裁切至384x438。我的疑惑在于,在使用真实观测图像进行测试时,裁切操作只是torch中的数组行列选取还是对整张图像进行指定步长采样操作?或者在测试时有其他方法可以通过尺寸为384训练的模型对660的观测进行直接重建?
from mst.
测试的时候不用裁剪,直接用660×660测。你可以看看我们的real测试代码
from mst.
非常感谢,祝您生活愉快!
from mst.
Related Issues (20)
- HSI可视化的问题 HOT 8
- 数据集无法下载 HOT 4
- TSA_simu_data/mask_3d_shift.mat 数据如何获得。 HOT 6
- trained model HOT 4
- self.HSI HOT 3
- 关于测试指标问题 HOT 4
- could you provide the training logs about DAUHST model? HOT 5
- Confusion about the code HOT 1
- 归一化问题 HOT 4
- 一点点疑问 HOT 3
- 论文real实验的图片 HOT 1
- ADMM-Net real result问题 HOT 3
- 关于real data的noise注入问题
- real and simulation dataset HOT 1
- 可视化生成的图片与作者百度网盘中提供的图片大小不一致 HOT 2
- About Layernorms within MSAB HOT 4
- 关于DAUHST HOT 1
- 请问MST这篇论文中的 图6是怎么画的? HOT 7
- 关于数据集的询问 HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from mst.