Comments (7)
Hi @Jacoobr,
-
Thanks for trying it out! You are correct, when you correct the vertex at time step T, the vertices before that do not get corrected. In our tool, when you drag a vertex, the vertices that get coloured red will not move. This is why the right method of interactive annotation is to move the first vertex that is wrong in the direction of RNN prediction to the right point (the direction is specified by the green line leaving from the green point in our tool).
-
We are working on preparing a training code release that is clean and modular. We will update on the repository as soon as it is done!
from polyrnn-pp.
Hi, if you're asking for the code for this, then it is in the works right now.
If you are asking theoretically, then the idea is to remember which time step the correct was made in, and force-feed the RNN the vertex at that time step, instead of letting it take its own output. Does that make sense?
from polyrnn-pp.
@amlankar Thanks for your reply, I can see the result when I correct one vertex that was produced not good by the RNN on your great online tools, then the whole vertexts except the vertext that I corrected will be reproduced (predicted) by the model nicely. But there are two questions as follows confused me: 1 ) I think when the vertex at time step t which corrected by me, then the next whole vertexs after time step t will be predicted once again depend on the vertext that I corrected. But the vertext before time step t should not be re-predicted because the lstm RNN net work is a sequence structure. In other words, the vertext corrected at time step t can't donate to the vertexts before that time step. How do you think of my thought? 2) Would you mind provide me with the training network? I plan to train my own model on MRI medical images data set about MRI segmentation task research. To be honest, I'm a newbie and I have taken a few days on trying to write the training network according to your CVPR 2018 paper. But I get nowhere about it. Your help will help me a lot with my work . This is my email address [email protected]. Look forward to your favourable reply. Thanks.
from polyrnn-pp.
Hi any timeline as to when will the correction code, as well as training code, will be made available? Thank you for releasing the inference code.
from polyrnn-pp.
It'll be released by the end of the summer
from polyrnn-pp.
@amlankar Wish for your work and the release of training codes. This work really marvelous!
from polyrnn-pp.
We have released the code now! Checkout https://github.com/fidler-lab/polyrnn-pp-pytorch
from polyrnn-pp.
Related Issues (20)
- Stride and Dilation? HOT 6
- demo error HOT 3
- Fine-tuning network HOT 5
- Visualizing the polygon model HOT 7
- demo not responding HOT 9
- while running demo on my own data HOT 8
- ValueError: graph_def is invalid at node u'GatherTree': Input types mismatch HOT 1
- Tensorflow version 18.11 HOT 2
- Regarding the values passed again to the pytorch model for the corrected coordinates HOT 2
- error downloading requirements HOT 1
- Jupyter project demo error
- how can I convert pth to ckpt HOT 3
- Does The Model Supports Annotation of Overlapping Masks?
- Demo not running HOT 1
- Upgrading to Pytorch 1.3/Python3 HOT 1
- Windows Scripts Needed
- the environment
- Tensorflow CPU Mode - Value Error in Demo Inference HOT 1
- Training code release date 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 polyrnn-pp.