This beach does not exist
StyleGAN2-ADA network trained in HD resolution 1280×768 on dataset with ~20.000 images of the beach. All of these beaches are AI-generated.
Website monitoring by
Testomato.com
Training progress
The video shows the learning process of the network. The learning progress is measured in kimg (kilo images). The network was trained for 25,000 kimg (until it had seen 25,000,000 images).
Random images
Generate random images
K-Means Clustering
Using the K-Means Clustering technique to group similar images. K-Means clustering is the unsupervised machine learning algorithm for image segregation.
Cluster #0/376
Download
Download the network for your experiments
or retrain the network with your own dataset (transfer learning).
Technical details
| Architecture | StyleGAN2-Ada |
|---|---|
| Dataset | 20.000 images |
| Network size | 362 MB |
| Resolution | 1280×768 px |
| Network layout | 5×3 squares (with 28 = 256 px sides) |
| Training options | --min_w=5 --min_h=3 --res_log2=8 |
| Trained using | RoyWheels/stylegan2-ada (github) |
| Training steps | 25.000 kimg |
| Metric FID50k | 3.14 |
Credits
This beach image generator was made using StyleGAN2-Ada, the GAN architecture published by researchers from NVIDIA Labs.
Paper: Training Generative Adversarial Networks with Limited DataOriginal implementation: NVlabs/stylegan2-ada
Non-square support and other improvements: RoyWheels/stylegan2-ada