A deep-learning package contains a set of well-organized deep-neural networks&tools.
Project description
pip install zhenglin
@ Author: zhenglin @version: 2.1.1
This package contains some off-the-shelves deep-learning networks implemented with .
use
pip install zhenglin
to install this package.
Major Updates
- Apr 30 2025:
Version: 2.1. Add a new class wrappervdclass undersupercv, supports video io now. - Apr 18 2025:
Version: 2.0. Add init version ofsupercv(cv)class, a universal wrapper for cv2, PIL.Image and Torch.tensor. - Nov 13 2024:
Version: 1.21. Package overhaul. Addhuggingface accelerate,pytorch-lightning, andpytorch-ddpsupport. - Oct 26 2023:
Version: 1.20.19AddTransUNetandSwinUNet - Aug 13 2023:
Version: 1.18.15Add basic quantization support.
Introduction
zhenglin package was motivated by eriklindernoren and his repo which provides many grab-and-run implementation of DL variants. It is the level ground for zhenglin's deep learning projects.
Specifically, this package provides
- universal structure-template:
zhenglin.accelerate.trainzhenglin.lightning.trainzhenglin.pytorch.train
- loss functions and metrics
zhenglin.utils.losszhenglin.utils.metrics
- utils
zhenglin.utils.utils
- 20 modular deep-learning networks
zhenglin.networks.*
Networks(Alphabetically):
- cycleGAN
- DDPM
- DeblurGAN
- EDSR
- ESRGAN
- Noise2Void
- Pix2Pix
- RCAN
- ResNet
- Restormer
- RRDBNet
- SqueezeNet
- SRDRM
- SRGAN
- SwinIR
- SwinUNet
- TransUNet
- U2Net
- UNet
- Attention-UNet
Project details
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