mjdietzx/SimGAN
Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training
This project helps computer vision researchers adapt models trained on synthetic eye images to perform well on real-world eye images. It takes a dataset of computer-generated eye images and a dataset of real human eye images as input. The output is an improved model for tasks like gaze estimation, making it more robust for use with actual human data. This tool is for researchers and engineers working on gaze tracking or similar applications who need to bridge the gap between simulated and real image data.
417 stars. No commits in the last 6 months.
Use this if you have a computer vision model trained on synthetic eye gaze data and need to make it perform accurately and reliably on real-world human eye images.
Not ideal if you are looking for a pre-trained, production-ready gaze estimation system, as this project focuses on the domain adaptation process itself.
Stars
417
Forks
98
Language
Python
License
MIT
Category
Last pushed
Jun 13, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mjdietzx/SimGAN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
huggingface/pytorch-pretrained-BigGAN
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
torchgan/torchgan
Research Framework for easy and efficient training of GANs based on Pytorch
metal3d/keras-video-generators
Keras generators to generate sequences from videos as input
GANs-in-Action/gans-in-action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch