honeygupta / uw-net Goto Github PK
View Code? Open in Web Editor NEWCode for paper [ICIP 2019] Unsupervised Single Image Underwater Depth Estimation (UW-Net)
License: MIT License
Code for paper [ICIP 2019] Unsupervised Single Image Underwater Depth Estimation (UW-Net)
License: MIT License
Hi,
Does the SI-MSE refer actually to MSE or RMSE?
Thank you
Thanks for your reply, but how did you prepare the ground-truth images. Since in evaluate_metrics.py line 41, the gt between 0-255 will all be converted to 0.
Originally posted by @jinjidelinmouren in #10 (comment)
How to finetune pre-trained model on new datasets? I have some new underwater images, I want to ask how to use the new RGB images fine-tuning pre-trained model. Do I need to use RGBD data when training?
If possible, would you mind provide the D-Hazy data set in the form of .npy files?
Hi!
I felt confused about the weights for each loss,
in your paper:
loss = 5Lgan + Lssim + Lgrad + Lcyc
however in the code, your loss is different:
loss = Lgan + 5Lcyc + 0.25Lssim(each) + Lgrad(only one for fakeA)
I'm coding on pytorch, and don't know how to set the weights for each loss.
Line 203 in b95324a
Best regards,
Dong
Hi, thank you for the code!
I wanted to ask you something about the dataset preparation. I have some terrestrial RGB images and their depth. I compose the RGBD images (about 30) and then I have 100 underwater images.
I'm getting NaN for all the losses and it doesn't change with the time.
I wanted to ask so do I need to pair the images in the csv file?
In your case, how much time it needed to converge?
Thank you in advance
Michele
Sorry to bother. I try to convert it with its evaluation code,but I don't know its max depth when I save it. Thanks.
hey, would it be possible for you to adjust the code so that I don't need .npy
files for testing? Or could you explain how to obtain the .npy
files (for testing)
Thank you for the pre-trained model and test code.
I'm a little confused about evaluating the Berman dateset. Could you please share the evaluation code for Berman you showed in Tab 1?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.