Comments (5)
Hi, can I evaluate the FVD result of 59.82 in your paper on the SKY dataset? Can you release your code and parameters for evaluating FVD?
Please refer to https://github.com/universome/stylegan-v.
from latte.
I used this code and the video generated by your pre-trained model to test the FVD, but I was only able to measure an FVD of 96.5, and could not reproduce the values of your paper. Can anyone teach me how to evaluate the FVD result of 59.82 in the paper on the SKY dataset?
from latte.
I used this code and the video generated by your pre-trained model to test the FVD, but I was only able to measure an FVD of 96.5, and could not reproduce the values of your paper. Can anyone teach me how to evaluate the FVD result of 59.82 in the paper on the SKY dataset?
Could you provide some more details about your evaluation of FVD?
from latte.
I generated 2048 16-frame videos with sample.sh using the provided pre-trained Latte checkpoint on sky, and then I converted the generated videos to frame-by-frame image format, followed by the stylegan-v/src/scripts/calc_metrics_for_dataset.py code in stylegan-v , I set the real video dir to be the image format of the sky dataset and set the fake video dir to be the image format of the video I generated. In calc_metrics_for_dataset.py I use the recommended default parameters from its readme. I choose to measure the metrics fvd2048_16f.
from latte.
I generated 2048 16-frame videos with sample.sh using the provided pre-trained Latte checkpoint on sky, and then I converted the generated videos to frame-by-frame image format, followed by the stylegan-v/src/scripts/calc_metrics_for_dataset.py code in stylegan-v , I set the real video dir to be the image format of the sky dataset and set the fake video dir to be the image format of the video I generated. In calc_metrics_for_dataset.py I use the recommended default parameters from its readme. I choose to measure the metrics fvd2048_16f.
Maybe you can adjust this parameter to match the training sample interval.
from latte.
Related Issues (20)
- Train code of t2v? HOT 2
- 如何复现主页展示的t2v效果? HOT 3
- t2v只支持16帧吗?我改成更多比如32帧就啥都看不到了 HOT 1
- Implementation of compression frame patch embedding (Fig. 3b) HOT 2
- diffusion noise modify HOT 1
- About Training Speed HOT 3
- About resume checkpoint HOT 2
- Extra key in ucf101.pt HOT 7
- Why choose these datasets and why not compare with pika, SVD or Gen2? HOT 1
- trained and sample result very strange (我自己训练复现的效果很奇怪) HOT 21
- What is the difference between Latte and ViViT? HOT 2
- RuntimeError: "LayerNormKernelImpl" not implemented for 'Half' HOT 2
- RuntimeError: "slow_conv2d_cpu" not implemented for 'Half' HOT 2
- image_size = [256,512] HOT 4
- CUDA out of memory HOT 4
- Some weights of AutoencoderKL were not initialized from the model checkpoint at /path/to/Latte/t2v_required_models/ and are newly initialized because the shapes did not match: HOT 3
- FaceForensics数据集 HOT 3
- No positional embeddings in LatteT2V?
- Is autoregression possible? HOT 3
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 latte.