Pinned Repositories
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
arcface_retinaface_mxnet2caffe
arcface and retinaface model convert mxnet to caffe, merge bn and scale into conv/fc.
arcface_retinaface_mxnet2onnx
arcface and retinaface model convert mxnet to onnx.
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
face.evoLVe.PyTorch
🔥🔥High-Performance Face Recognition Library on PyTorch🔥🔥
HyperDL-Tutorial
深度学习教程整理 | 干货
insightface
Face Recognition Project on MXNet
libfacedetection
An open source library for face detection in images. The face detection speed can reach 1500FPS.
License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
works in real-time with detection and recognition accuracy up to 99.8% for Chinese license plates: 100 ms/plate
SeetaFace2
SeetaFace 2
zheshipinyinMc's Repositories
zheshipinyinMc/arcface_retinaface_mxnet2onnx
arcface and retinaface model convert mxnet to onnx.
zheshipinyinMc/arcface_retinaface_mxnet2caffe
arcface and retinaface model convert mxnet to caffe, merge bn and scale into conv/fc.
zheshipinyinMc/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
zheshipinyinMc/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
zheshipinyinMc/face.evoLVe.PyTorch
🔥🔥High-Performance Face Recognition Library on PyTorch🔥🔥
zheshipinyinMc/HyperDL-Tutorial
深度学习教程整理 | 干货
zheshipinyinMc/insightface
Face Recognition Project on MXNet
zheshipinyinMc/libfacedetection
An open source library for face detection in images. The face detection speed can reach 1500FPS.
zheshipinyinMc/License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
works in real-time with detection and recognition accuracy up to 99.8% for Chinese license plates: 100 ms/plate
zheshipinyinMc/SeetaFace2
SeetaFace 2