kasyoukin's repositories
ACNet
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
EAST
A tensorflow implementation of EAST text detector
FastMaskRCNN
Mask RCNN in TensorFlow
image_retrieval
Image retrieval system demo based on caffe and lsh
mx-maskrcnn
An MXNet implementation of Mask R-CNN
openface
Face recognition with deep neural networks.
Paddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Paddle-Lite
Multi-platform high performance deep learning inference engine (『飞桨』多平台高性能深度学习预测引擎）
PyTorch-Encoding
A PyTorch CV Toolkit
ResNeSt
ResNeSt: Split-Attention Network
RFCN-tensorflow
RFCN implementation in TensorFlow
RRPN
Source code of RRPN ---- Arbitrary-Oriented Scene Text Detection via Rotation Proposals
SCUT-EPT_Dataset_Release
The SCUT-EPT Dataset for the research of offline handwritten Chinese text recognition (HCTR) in educational documents has been released.
TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a lightweight and high-performance deep learning framework for mobile inference. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework. TNN：由腾讯优图实验室和光影实验室协同打造，移动端高性能、轻量级推理框架，同时拥有跨平台、高性能、模型压缩、代码裁剪等众多突出优势。TNN框架在原有Rapidnet、ncnn框架的基础上进一步加强了移动端设备的支持以及性能优化，同时也借鉴了业界主流开源框架高性能和良好拓展性的优点。目前TNN已经在手Q、微视、P图等应用中落地，欢迎大家参与协同共建，促进TNN推理框架进一步完善。
WGAN-tensorflow
a tensorflow implementation of WGAN
X2Paddle
Deep learning model converter for PaddlePaddle. (『飞桨』深度学习模型转换工具)
yolov5
YOLOv5 in PyTorch > ONNX > CoreML > iOS