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dilation

Dilated Convolution for Semantic Image Segmentation

dl-docker

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

dlsm

Deep Learning Study Group

dltk

Deep Learning Toolkit for Medical Image Analysis

dm-docker

Base and example Docker images for the Digital Mammography DREAM Challenge

dni-tensorflow

DNI(Decoupled Neural Interfaces using Synthetic Gradients) implementation with Tensorflow

dsb-2017

3D CNN on LUNA16，Data Science Bowl 2017， neon

dsb2017

Code for 2nd place solution to the 2017 National Data Science Bowl

dsb2017-1

The solution of team 'grt123' in DSB2017

dsb3

dsn_tensorflow

Implementation of Deeply-Supervised Networks in Tensorflow (http://vcl.ucsd.edu/~sxie/pdf/dsn_nips2014.pdf)

dss

code for "Deeply supervised salient object detection with short connections" published in CVPR 2017

dtn-tensorflow

domain transfer network. tensorFlow implementation of unsupervised cross-domain image generation

dynet

DyNet: The Dynamic Neural Network Toolkit

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emotion-recognition-neural-networks

Emotion recognition using DNN with tensorflow

emphysema-estimation

end-to-end-for-chinese-plate-recognition

多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet

end2end-all-conv

End-to-end training for breast cancer diagnosis using deep all convolutional networks

enet

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

face_classification

Face detection and emotion classification using fer2013 dataset and a keras CNN model.

face_object_recognition

a project to implement face/object recognition in a robot

face_recognition

The world's simplest facial recognition api for Python and the command line

face_recognition_models

Trained models for the face_recognition python library

facehack

2D入力からの3D表情転写システム

facenet

Face recognition using Tensorflow

fast-layernorm-tf

Efficient layer normalization GPU kernel for Tensorflow

fast-style-transfer

Fast Style Transfer in TensorFlow ⚡🖥🎨🖼

faster_r_cnn

theano Implementation of "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"

feature-extraction

Computer vision feature extraction toolbox for image classification