ieee820

PPGNet

Source code for our CVPR 2019 paper - PPGNet: Learning Point-Pair Graph for Line Segment Detection

surface-defect-detection

缺陷检测文献记录

TIES-2.0

Code for: S.R. Qasim, H. Mahmood, and F. Shafait, Rethinking Table Parsing using Graph Neural Networks (2019)

Deep-Learning-Approach-for-Surface-Defect-Detection

（最先进的缺陷检测网络） A Tensorflow implementation of "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection"

faiss

A library for efficient similarity search and clustering of dense vectors.

semantic-segmentation-pytorch

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

A-Deep-Learning-Based-Approach-For-Fast-And-Robust-Steel-Surface-Defects-Classification

Aggregation-Cross-Entropy

Aggregation Cross-Entropy for Sequence Recognition. CVPR 2019.

Bearing-fault-detection

轴承故障检测 训练赛第30名代码

C-OCR

C-OCR是携程自研的OCR项目，主要包括身份证、护照、火车票、签证等旅游相关证件、材料的识别。 项目包含4个部分，拒识、检测、识别、后处理。

captcha_platform

[验证码识别-部署] This project is based on CNN+BLSTM+CTC to realize verificationtion. This projeccode identificat is only for deployment models.

captcha_trainer

[验证码识别-训练] This project is based on CNN5/ResNet+BLSTM/LSTM/GRU/SRU/BSRU+CTC to realize verification code identification. This project is only for training the model.

ctcdecode

PyTorch CTC Decoder bindings

CTCWordBeamSearch

Connectionist Temporal Classification (CTC) decoder with dictionary and language model for TensorFlow.

Diabetic-Retinopathy-Feature-Extraction-using-Fundus-Images

Diabetic Retinopathy is a very common eye disease in people having diabetes. This disease can lead to blindness if not taken care of in early stages, This project is a part of the whole process of identifying Diabetic Retinopathy in its early stages. In this project, we'll extract basic features which can help us in identifying Diabetic Retinopathy in its early stages.

HRNet-Semantic-Segmentation

High-resolution representation learning (HRNets) for Semantic Segmentation

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.

keras-bert

Implementation of BERT that could load official pre-trained models for feature extraction and prediction

keras-gpt-2

Load GPT-2 checkpoint and generate texts

mace

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

NPH_Prediction

Code to accompany NPH Prediction paper.

pix2pixHD

Synthesizing and manipulating 2048x1024 images with conditional GANs

SaCNN-CrowdCounting-Tencent_Youtu

Crowd Counting Via Scale-adaptive Convolutional Neural Network

serving

A flexible, high-performance serving system for machine learning models

shennong

A Python toolbox for unsupervised speech recognition

Table-Detection-using-Deep-learning

Tensorflow, Luminoth Based Table Detection and Extraction

Tensorflow-TensorRT

This repository is for my YT video series about optimizing a Tensorflow deep learning model using TensorRT. We demonstrate optimizing LeNet-like model and YOLOv3 model, and get 3.7x and 1.5x faster for the former and the latter, respectively, compared to the original models.

TensorRT

TensorRT is a C++ library that facilitates high performance inference on NVIDIA GPUs and deep learning accelerators.

TIoU-metric

Tightness-aware Evaluation Protocol for Scene Text Detection (CVPR 2019)