YDDDDG's starred repositories
OpenCVTutorials
OpenCV-Python4.1 中文文档
advanced-java
😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲:涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识
PaperReadingForDeepLearning
Paper Reading for DeepLearning
pytorch-Learning-to-See-in-the-Dark
Learning to See in the Dark using PyTorch >= 1.0.0
Intellij-Colors-Sublime-Monokai
Syntax color theme for JetBrains products inspired by Sublime Text's Monokai Theme
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Awesome-Super-Resolution
Collect super-resolution related papers, data, repositories
micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
Knowledge-Distillation-Zoo
Pytorch implementation of various Knowledge Distillation (KD) methods.
EDSR-PyTorch
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
Awesome-ECCV2024-ECCV2020-Low-Level-Vision
A Collection of Papers and Codes for ECCV2024/ECCV2020 Low Level Vision
roLabelImg
Label Rotated Rect On Images for training
Self-Guided-Network-for-Fast-Image-Denoising
The PyTorch implementation of SGN (ICCV 2019), modified from https://github.com/zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising
Self-Guided-Network-for-Fast-Image-Denoising
The PyTorch implementation of ICCV 2019 paper SGN
Real_Time_Image_Animation
The Project is real time application in opencv using first order model