inception-score
There are 17 repositories under inception-score topic.
mseitzer/pytorch-fid
Compute FID scores with PyTorch.
toshas/torch-fidelity
High-fidelity performance metrics for generative models in PyTorch
w86763777/pytorch-image-generation-metrics
Pytorch implementation of common image generation metrics.
lzhbrian/metrics
IS, FID score Pytorch and TF implementation, TF implementation is a wrapper of the official ones.
tsc2017/Inception-Score
CPU/GPU/TPU implementation of the Inception Score
GongXinyuu/sngan.pytorch
An unofficial Pytorch implementation of SNGAN, achieving IS of 8.21 and FID of 14.21 on CIFAR-10.
nnUyi/Inception-Score
Inception score for measuring quality of images generating from GAN
AliceAria/Performance-comparison-of-GAN-on-cifar-10
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10
ChenLiu-1996/GAN-evaluator
A pip-installable evaluator for GANs (IS and FID). Accepts either dataloaders or individual batches. Supports on-the-fly evaluation during training. A working DCGAN SVHN demo script provided.
kozistr/gan-metrics
Lots of evaluation metrics for the generative adversarial networks in pytorch
kvpratama/gan_metrics
Metrics to evaluate GAN
giocoal/CXR-ACGAN-chest-xray-generator-covid19-pneumonia
CXR-ACGAN: Auxiliary Classifier GAN (AC-GAN) for Chest X-Ray (CXR) Images Generation (Pneumonia, COVID-19 and healthy patients) for the purpose of data augmentation. Implemented in TensorFlow, trained on COVIDx CXR-3 dataset.
KimRass/DDPM
PyTorch implementation of 'DDPM' (Ho et al., 2020) and training it on CelebA 64×64
michaelklachko/generative-models
Pytorch implementation of popular generative models
nirmal-25/Text-to-Image-GAN
Implementation of GAN-based text-to-image models for a comparative study on the CUB and COCO datasets
vrai-group/guided-infant-generation
GAN-based framework to generate depth images of infants from a desired image and pose
SelmaGuedidi/generating-images-with-dcgan
This GitHub repository contains an implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) for image generation. With this project, you can generate stunning and realistic images using the power of deep learning.