DLR-MI/atme

Official pytorch implementation of ATME [CVPR 2023 GMCV]

29
/ 100
Experimental

This project helps computer vision researchers and practitioners transform images from one domain to another, such as converting satellite maps into street views or day scenes into night scenes. You provide a dataset of paired images, and it outputs a model that can generate new images in the target style. This is ideal for those working on generative image tasks.

No commits in the last 6 months.

Use this if you need to perform high-quality image-to-image translation for tasks like converting sketches to photos, altering facial expressions, or changing environmental conditions in images.

Not ideal if you don't have paired datasets for your image transformation task, as this method requires input images with corresponding output examples.

image-to-image translation generative AI computer vision image synthesis machine learning research
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 6 / 25

How are scores calculated?

Stars

29

Forks

2

Language

Jupyter Notebook

License

Last pushed

Nov 02, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/DLR-MI/atme"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.