megmodels

Models for megengine

Models

The Correspondence:

path	name in code	paper title
dncnn	dncnn_25	Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
n2n	n2n_gauss25, n2n_gauss5to50, n2n_poisson30, n2n_poisson5to50	Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
nts	attention_net	Learning to Navigate for Fine-grained Classification
esrgan	rrdb_psnr, rrdb_esrgan	ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
rcan	rcan_x2, rcan_x3, rcan_x4	Image Super-Resolution Using Very Deep Residual Channel Attention Networks
csd	csd_edsr_mix, csd_edsr_student	Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
rdn	rdn_x2, rdn_x3, rdn_x4	Residual Dense Network for Image Super-Resolution

Pretrained pytorch models:

• Link: https://pan.baidu.com/s/1-pw6tUwmU0Q0uFbU-K2p1A code: y1md

Thanks

Thanks to the following authors for generously making their code available.

• DnCNN : https://github.com/cszn/DnCNN
• N2N : https://github.com/TaoHuang2018/Neighbor2Neighbor
• NTS-Net : https://github.com/yangze0930/NTS-Net
• ESRGAN : https://github.com/xinntao/ESRGAN
• RCAN : https://github.com/yulunzhang/RCAN
• CSD : https://github.com/Booooooooooo/CSD
• RDN : https://github.com/yulunzhang/RDN https://github.com/open-mmlab/mmediting