Comments (1)
@chrismcruiz
Hi, the released model is based on the proposed VAC, which is the non-iterative approach. We have not conducted cross dataset experiments yet. From my understanding, the feature extractor can be applied on another dataset, you need to change the dataloader and adopt the "features" mode, which will extract frame-wise features.
However, the posture distribution from different datasets may be different, maybe it will not achieve optimal performance, it is better to finetune or train on another dataset.
from vac_cslr.
Related Issues (20)
- Torch not compiled with CUDA enabled HOT 2
- Error when i run the training part main.py HOT 1
- unable to run successfully when I run main.py HOT 1
- unable to successfully install CTC HOT 5
- Detailed code running steps HOT 19
- Visualizing the predicted alignments HOT 1
- unable to run these repository on google colab HOT 1
- Data augmention error HOT 4
- about the feature extractor architecture HOT 6
- Successfully inference, but unable to train HOT 3
- Visualize Epoch vs Loss HOT 1
- Hardware and Software Specifications for this research. HOT 3
- Gloss Segment Boundary Assignment Algorithm (GSBA) HOT 1
- Does your PHOENIX-2014-T dataset have /1/ folder? HOT 1
- model or model.parameters()? HOT 2
- Does your model produce different results each time it is run? HOT 4
- Training the baseline (without VAC, SMKD) HOT 2
- How can I use a trained model to test a video?
- unable to reconize any word but the loss is decreasing??? HOT 1
- Does preprocess.sh have an effect if using datasets other than phoneix? 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 vac_cslr.