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).
Generate random images
Using the K-Means Clustering technique to group similar images. K-Means clustering is the unsupervised machine learning algorithm for image segregation.
Download the network for your experiments
or retrain the network with your own dataset (transfer learning).
|Network size||362 MB|
|Network layout||5×3 squares (with 28 = 256 px sides)|
|Trained using||RoyWheels/stylegan2-ada (github)|
|Training steps||25.000 kimg|
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 Data
Original implementation: NVlabs/stylegan2-ada
Non-square support and other improvements: RoyWheels/stylegan2-ada