Comments (5)
It is intended. Note that one of our key features is that we only need to train on low-resolution images with the largest being around 400x500 while extending to high-resolution only in evaluation. You should not be using the online dataset for large images. If you have sufficiently many annotated high-resolution training images, this work is probably not the most suitable (GLNet might be better in that situation).
from cascadepsp.
Thanks for reporting.
For 2 it is probably due to the new torchvision version, see pytorch/vision#2520.
One quick fix is to revert to torchvision 0.6.0.
I will probably fix these soon.
from cascadepsp.
Got it.
By the way, it looks like the way the code handles the dataset, you look at random crops of 224 pixels. This is surprising because the meaning of 224 pixels may be either very broad context if the original image is pretty small, or a tiny patch takes completely out of context if the original image was, say, 4000X4000 pixels. Is this intended behavior?
from cascadepsp.
Right. It's certainly counter-intuitive that training on one scale and predicting on another would yield such impressive results! I guess I need to reread what you wrote on this in more depth.
from cascadepsp.
Fixed.
from cascadepsp.
Related Issues (20)
- Error while running demo code!! HOT 4
- How to change epoch? HOT 3
- Segmentation Refinement Runtime Error HOT 11
- Train the Net HOT 4
- How to implement 'Local step' only? HOT 2
- how to solve this problem? HOT 2
- refinement is not a binary image HOT 1
- Can this method be used as post-processing for video object segmentation? HOT 2
- local step only HOT 4
- Help with Colab/local setup HOT 3
- Tree leaves boundary refinement HOT 5
- hashlib error HOT 1
- weight coefficient
- Very slow prediction, high cpu /gpu usage HOT 1
- Car Window Refinement
- Whether this model can be applied to Cityscapes datasets? HOT 1
- How to train PSPNet/DeepLabV3+ just as the result in your paper HOT 12
- Any low hanging fruit for generation speed improvements? HOT 2
- About the problem of dealing with redundant incomplete masks HOT 2
- Query Regarding High-Resolution Image Segmentation with CascadePSP 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 cascadepsp.