stargan-v2 | StarGAN v2 - Official PyTorch Implementation | Machine Learning library

by clovaai Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | stargan-v2 Summary

stargan-v2 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. stargan-v2 has no bugs, it has no vulnerabilities and it has medium support. However stargan-v2 build file is not available and it has a Non-SPDX License. You can download it from GitHub.

StarGAN v2: Diverse Image Synthesis for Multiple Domains Yunjey Choi*, Youngjung Uh*, Jaejun Yoo*, Jung-Woo Ha In CVPR 2020. (* indicates equal contribution). Paper: Video: Abstract: A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain variations. The code, pre-trained models, and dataset are available at clovaai/stargan-v2.

Support

Quality

Security

License

Reuse

Support

stargan-v2 has a medium active ecosystem.

It has 3227 star(s) with 631 fork(s). There are 82 watchers for this library.

It had no major release in the last 6 months.

There are 96 open issues and 50 have been closed. On average issues are closed in 62 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of stargan-v2 is current.

Quality

stargan-v2 has 0 bugs and 0 code smells.

Security

stargan-v2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

stargan-v2 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stargan-v2 has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

stargan-v2 releases are not available. You will need to build from source code and install.

stargan-v2 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

stargan-v2 saves you 706 person hours of effort in developing the same functionality from scratch.

It has 1641 lines of code, 126 functions and 12 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed stargan-v2 and discovered the below as its top functions. This is intended to give you an instant insight into stargan-v2 implemented functionality, and help decide if they suit your requirements.

Train the model
Calculate evaluation metrics
Create a DataLoader for the evaluation phase
Calculate the FID for each task
Align the faces
Align images to image
Get the landmarks from the heatmap
Get preds from hm
Outputs the visualization of an image
Translate images using latent variables
Translate images using a reference source
Translate and reconstruct image
Forward computation
Create a data loader for training images
Align all the faces
Create a dataLoader for the generation of the dataset
Generate a video latent layer
Interpolate heatmap
Save images to fname
Generate reference frames
Create a canvas from a list of alphas
Creates a DataLoader for training images
Makes a weighted random sample from the given labels
Calculate the gradient of the Gaussian function
Create a dataset
Calculate metrics
Sample from loaders
Create a data loader for the generation of the image
Forward transformation

Get all kandi verified functions for this library.

stargan-v2 Key Features

No Key Features are available at this moment for stargan-v2.

stargan-v2 Examples and Code Snippets

Usage,2. Dataset Structure

Python

Lines of Code : 9

License : Permissive (MIT)

Copy

root/ants/xxx.png
root/ants/xxy.jpeg
root/ants/xxz.png
.
.
.
root/bees/123.jpg
root/bees/nsdf3.png
root/bees/asd932_.png

Preparing datasets

Python

Lines of Code : 7

License : Non-SPDX (NOASSERTION)

Copy

git submodule update --init --recursive

cd dataset_preprocessing/ffhq
python runme.py

cd dataset_preprocessing/afhq
python runme.py "path/to/downloaded/afhq.zip"

cd dataset_preprocessing/shapenet
python runme.py

Usage,5. Pretrained model

Python

Lines of Code : 5

License : Permissive (MIT)

Copy

$ bash download.sh pretrained-celeba-256x256

$ python main.py --mode test --dataset CelebA --image_size 256 --c_dim 5 \
                 --selected_attrs Black_Hair Blond_Hair Brown_Hair Male Young \
                 --model_save_dir='stargan_celeba

Community Discussions

Trending Discussions on stargan-v2

CUDA out of memory in Google Colab

QUESTION

CUDA out of memory in Google Colab

Asked 2020-Nov-16 at 23:18

I am trying to replicate a GAN study. So, I want to train a model (using less data) in Google Colab. But, I got this problem:

...

ANSWER

Answered 2020-Nov-16 at 16:30

If you aren't using the Pro version of Google Colab, then you're going to run into somewhat restrictive maximums for your memory allocation. From the Google Colab FAQ...

The amount of memory available in Colab virtual machines varies over time (but is stable for the lifetime of the VM)... You may sometimes be automatically assigned a VM with extra memory when Colab detects that you are likely to need it. Users interested in having more memory available to them in Colab, and more reliably, may be interested in Colab Pro.

You already have a good grasp of this issue, since you understand that lowering batch_size is a good way to get around it for a little while. Ultimately, though, if you want to replicate this study, you'll have to switch to a training method that can accommodate for the amount of data you seem to need.

Source https://stackoverflow.com/questions/64861682

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install stargan-v2

You can download it from GitHub.
You can use stargan-v2 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: