Comments (1)
For GTA5->cityscapes experiment, we actually trained from scratch during intra-da stage. As we compare "training from scratch" and "resume from inter-da", there is not much difference in segmentation task. But it is possible to be different in other experiments. For the quality of masks of easy samples, it really affects the final results so we suggest you to get cleaner maps for easy sample.
from intrada.
Related Issues (16)
- is there a batch size of 1 per gpu? HOT 4
- pretrained model for resnet50 backbone HOT 3
- low performance HOT 8
- How to implement from Synscapes/SYNTHIA to Cityscapes? HOT 1
- Entropy Maps HOT 1
- The initial parameters of the model for intra-domain adaptation HOT 1
- About trained model of ADVENT HOT 2
- how to train model on custom dataset HOT 7
- Two-phase Pseudo Label Densification for Self-training based Domain Adaptation HOT 2
- About Pretrained and evaluation models HOT 1
- Could you update the entire training code of intraDA? HOT 1
- Who can share me the pretrained model, the download url was broken.
- Can't get the 47% mIoU
- 目标域没有标签 HOT 7
- No color_masks HOT 1
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 intrada.