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2018_04_19_surfacereconstruction

Structured light 3D scanning using multiple frequency phase shifting

attentionocr

Scene text recognition

background-matting

Background Matting: The World is Your Green Screen

bbox-visualizer

Different ways of object detection visualizations, given bounding box location

crackforest

crackforest-dataset

craft-pytorch

Pytorch implementation of CRAFT text detector

cvpr2019

cvpr2019 papers

daily-paper-computer-vision

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

deblurgan

Image Deblurring using Generative Adversarial Networks

deep-image-retrieval

End-to-end learning of deep visual representations for image retrieval

deep_motion_mag

Tensorflow implementation of Learning-based Video Motion Magnification

deepdiy

Deep learning based tool for image processing. No need for Programing and GPU.

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

deeppcb

A PCB defect dataset.

defocus_segmentation

LBP-based segmentation of defocus blur

deye

Keep an Eye on Defects Inspection.

dfdrelblur

Relative blur based Depth from Defocus (DFD)

dpsr

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)

dsegmentation

This repos contains all implementations used in the paper "MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS" accepted at ICIP 2018.

edges

Structured Edge Detection Toolbox

face-track-detect-extract

💎 Detect , track and extract the optimal face in multi-target faces (exclude side face and select the optimal face).

fairmot

A simple baseline for one-shot multi-object tracking

faster-mobile-retinaface

Reimplementation of retinaface, faster and stronger.

gauss

code repo for gauss6500

gluon-cv

Gluon CV Toolkit

gpt2-chinese

Chinese version of GPT2 training code, using BERT tokenizer.

halide-hls

HLS branch of Halide

httpsvr

http1.1 https http2 all of the version are surported by the software c++14

icdar2019_ctdar

The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.