LanWong1's repositories
chinese_ocr
CTPN + DenseNet + CTC based end-to-end Chinese OCR implemented using tensorflow and keras
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
AttentionOCR
Scene text recognition
CenterNet
Object detection, 3D detection, and pose estimation using center point detection:
coursera-ml-py
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
CRNN_Chinese_Characters_Rec
(CRNN) Chinese Characters Recognition.
CV_interviews_Q-A
CV算法岗知识点及面试问答汇总,主要分为计算机视觉、机器学习、图像处理和 C++基础四大块,一起努力向offers发起冲击!
DB
A PyToch implementation of "Real-time Scene Text Detection with Differentiable Binarization".
DBNet.pytorch
A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization
DifferentiableBinarization
DB (Real-time Scene Text Detection with Differentiable Binarization) implementation in Keras and Tensorflow
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
faceDetect-ios
fast face detect
interview
📚 C/C++ 技术面试基础知识总结,包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。
Leading-Papers-Robustness-Oriented
Some leading papers in deep learning, which focused on robustness. I think it will be helpful to NeuralFinance.
learnopencv
Learn OpenCV : C++ and Python Examples
PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
pytorch-YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4
pytorchOCR
基于pytorch的ocr算法库,包括 psenet, pan, dbnet, sast , crnn
Real-time-Text-Detection
PyTorch re-implementation of ''Real-time Scene Text Detection with Differentiable Binarization'' (AAAI 2019)
TableNet
Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"
TensorExpand
集成包
text-detection-ctpn
text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network
YOLOv3-model-pruning
对 YOLOv3 做模型剪枝(network slimming),对于 oxford hand 数据集(因项目需要),模型剪枝后的参数量减少 80%,Infer 的速度达到原来 2 倍,mAP 基本不变