michaelshing,github

3ddfa

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

3ddfa_v2

The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.

3dmm_cnn

Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network

adaptivewienerfilter

C++ Implementation of the Adaptive Wiener Filter (Based on Opencv Library)

anomalydetection-and-recommendersystems

Final assignment in Andrew Ng's Machine Learning MOOC on the topics of anomaly detection and recommender systems

apc-vision-toolbox

MIT-Princeton Vision Toolbox for the Amazon Picking Challenge 2016 - RGB-D ConvNet-based object segmentation and 6D object pose estimation.

awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-rl

Reinforcement learning resources curated

awesome_3dreconstruction_list

A curated list of papers & resources linked to 3D reconstruction from images.

caffe

Caffe: a fast framework for deep learning. For the most recent version checkout the dev branch. For the latest stable release checkout the master branch.

caffe-l1_loss_layer

Implementation of L1 Loss Layer in Caffe.

cascaded_mobilenet-v2

cascaded convolutional neural network for facial point detection

cnn-facial-landmark

Facial landmark detection based on deep convolutional neural network.

cnn-models

ImageNet pre-trained models with batch normalization for the Caffe framework

cnn_lstm_ctc_tensorflow

CNN+LSTM+CTC based OCR implemented using tensorflow.

Convolutional Neural Networks for Matlab. Has versions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identically. The GPU version uses kernels from Alex Krizhevsky's library 'cuda-convnet2'.

coursera_recommender_system

A recommender System which studies profiles of the user, combines results from collaborative filtering and recommends courses to the visitor .

crab

Crab is a ﬂexible, fast recommender engine for Python that integrates classic information ﬁltering recommendation algorithms in the world of scientiﬁc Python packages (numpy, scipy, matplotlib).