162050079's Stars
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
BVLC/caffe
Caffe: a fast open framework for deep learning.
julycoding/The-Art-Of-Programming-By-July-2nd
本项目曾冲到全球第一,干货集锦见本页面最底部,另完整精致的纸质版《编程之法:面试和算法心得》已在京东/当当上销售
yuanguangxin/LeetCode
LeetCode刷题记录与面试整理
artix41/awesome-transfer-learning
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
ry/tensorflow-resnet
ResNet model in TensorFlow
hszhao/PSPNet
Pyramid Scene Parsing Network, CVPR2017.
ethanhe42/channel-pruning
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
thuml/Xlearn
Transfer Learning Library
Ao-Lee/Vgg-Face-Fine-tune
fine tune a pre-trained vgg face using triplet loss in keras
lidian007/EmotiW2016
jbhuang0604/WSL
Weakly Supervised Object Localization with Progressive Domain Adaptation (CVPR 2016)
xinghedyc/mxnet-cnn-lstm-ctc-ocr
This repo contains code written by MXNet for ocr tasks, which uses an cnn-lstm-ctc architecture to do text recognition.
ThomasDelteil/TextClassificationCNNs_MXNet
CNN, NLP and MXNet/Gluon demo
bknyaz/emotiw
code for Emotion Recognition in the Wild (EmotiW) challenge
xujinchang/EmotiW-2017-Audio-video-Emotion-Recognition
Method strategy for EmotiW 2017 video emotion recognition
erinhp/SSE
Learning Supervised Scoring Ensemble for Emotion Recognition in the Wild, EmotiW 2017, ICMI 2017
gaussic/text-classification
CNN for sentence classification using Pytorch and MXNET
anujshah1003/Transfer-Learning-Face-Recognition-using-VGG-16
This repository shows how we can use transfer learning in keras with the example of training a face recognition model using VGG-16 pre-trained weights
ebadawy/EmotiW2017
Dengshunge/machine_learning
代码主要是《机器学习实战》,转化为了3.6版本的
wut0n9/cnn_chinese_text_classification
运用cnn + highway network网络结构中文文本分类
prabhuiitdhn/Emotion-detection-VGGnet-architecture-fer2013
I have used haarcascade.xml and deep learning techniques to detect the emotion of person. Trained in keras framework using fer2013 dataset. I have delivered the model with almost 95.0% of accuracy. Credit: PyImageSearch
tadax/cvtools
Tools for Computer Vision
rahulkishan-mobbed/VGGFace_caffe