Comments (8)
The code is tested in Ubuntu 16.04. It is implemented based on MXNet 1.3.0 and Python 2.7.12. For GPU usage, the maximum GPU memory consumption is about 7GB in a single NVIDIA TiTan Xp.
from cbst.
Well, this doesn't really answer to the question. However, I implemented my own version of the method in pytorch. Thank you anyways.
from cbst.
@fabvio May i know if you got same result as mentioned in this paper? Can you share the code in pytorch version? Many thanks if you can sharing the code.
from cbst.
I didn't test it directly on the dataset used for the paper, but I have to say that the results were good. Unfortunately my code is IP restricted, but it wasn't difficult to implement it, so I definitely advise you to give it a try.
from cbst.
OK, many thanks for your information!
from cbst.
@jiaxing002 Check out our new paper "Confidence Regularized Self-Training" (ICCV 2019, Oral) (https://arxiv.org/pdf/1908.09822.pdf), which investigates confidence regularization in self-training systematically. The pytorch code based on CBST will be released soon before ICCV 2019.
from cbst.
Hi Guys. Is the pytorch code coming soon? we are eagerly waiting for it. Thanx for letting us know.
from cbst.
Hi Guys. Is the pytorch code coming soon? we are eagerly waiting for it. Thanx for letting us know.
Check our released pytorch code for GTA2Cityscapes: https://github.com/yzou2/CRST
from cbst.
Related Issues (16)
- VGG16 code HOT 2
- simple_bind error HOT 11
- About the basenet HOT 2
- SYNTHIA-RAND-CITYSCAPES dataset HOT 1
- Prior_array.mat file generation HOT 2
- Save confidence scores of target domain as class-wise vectors HOT 2
- File mine_id.npy and mine_id_priority.npy
- unable to download SYNTHIA-RAND-CITYSCAPES dataset
- pretrained file corrupted HOT 4
- About the Results HOT 1
- Training Problem HOT 9
- About the finetuning of self-training HOT 2
- About label definition of SYNTHIA HOT 1
- About label format of GTA HOT 1
- BDD-V HOT 4
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 cbst.