Comments (3)
Hi there,
Thanks for your interest. There are albeit minor differences but I can assure you none of these will result in significant deviation in inference. Again thanks for your pointer and I will look to tighten the gap in future repos.
from visualtransformers.
Thanks for your reply! I still have some questions regarding the original paper of Visual Transformer, hopefully you could help me out.
1.In the paper, they set the token channel size to 1024. However, according to Fig.2 and the residual adding in equation 3 and 4, I assume the channel size is always the same with input feature map channel size (for ResNet18&34, the channel size is 256).
2. They adopt transformer encoder, however it seems to me that no multi-head attention is used from equation 3.
I appreciate it very much if you could help me out.
from visualtransformers.
Never mind, the first version paper have help me out.
from visualtransformers.
Related Issues (12)
- Static Tokenization HOT 3
- Performances
- Mask shape is not correct HOT 1
- Only 'BasicBlock',not have 'Bottleneck'
- Hi, regarding the nn1 of ViTResNet: HOT 3
- Semantic Segmentation HOT 1
- No Documentation! HOT 3
- some questions HOT 6
- Question on token HOT 3
- Did u training a smeantic segmentation example using this model?aa
- Classification tokens 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 visualtransformers.