FrancisYizhang

convnet-study

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

deep-base

Deep learning base image for Docker (Tensorflow, Caffe, MXNet, Torch, Openface, etc.)

deep-learning-models

Keras code and weights files for popular deep learning models.

deep_share

Reference implementation of "Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification"

DenseNet

Code for Densely Connected Convolutional Networks (DenseNets)

DenseNet-1

DenseNet implementation in Keras

DenseNet-Caffe

DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet

DenseNet-Keras

DenseNet Implementation in Keras with ImageNet Pretrained Models

DMTRL

[Code] Deep Multi-task Representation Learning: A Tensor Factorisation Approach

facepp-matlab-sdk

Matlab SDK for Face++

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_right:

gpu_flow

GPU based optical flow extraction in OpenCV

head-pose-estimation-and-face-landmark

head pose estimation

HumanPoseEstimation

Human Pose Estimation using Convolution Net on Matconvnet

keras

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

keras-spp

Spatial pyramid pooling layers for keras

matconvnet

MatConvNet: CNNs for MATLAB

matconvnet-fcn

A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation

ResNet-Matconvnet

Train Deep Residual Network from Scratch or or Fine-tune Pre-trained Model using Matconvnet

RstarCNN

R*CNN

TCDCN-face-alignment

Matlab implementation of facial landmark detection by deep multi-task learning

tensorflow

Computation using data flow graphs for scalable machine learning

tensornets

High level network definitions with pre-trained weights in TensorFlow

twostreamfusion

Code release for "Convolutional Two-Stream Network Fusion for Video Action Recognition", CVPR 2016.

wide-resnet.pytorch

Best CIFAR-10, CIFAR-100 results with wide-residual networks using PyTorch