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tripletloss-keras-tensorflow

An example implementation of triplet-loss in tensorflow using keras

tutorials

tutorials-1

机器学习相关教程

u-ide_blacke

Classifying emotion by using estimated keypoints instead of full images

ub-deeplearning

Investigation Code from my Deep Learning internship in University of Barcelona, Jan-Jun 2018

ucf-101_video_classification

Classify UCF101 videos using one frame at a time with a CNN(InceptionV3)

understanding-recurrent-convolutional-neural-networks

Using synthetically generated video data to learn how/when R-CNNs can outperform CNNs.

unified-face-landmark-and-gender-recognition

Face Detection, landmark detection and gender classification in a unified workflow

urban-sound-challenge

My Urban Sound Challenge classification

urban-sound-classification

Urban sound classification using Deep Learning

urban-sound-classification-keras

urban-sound-co2

Urban Sound Challenge project

using-python-for-research

using-python-for-research-1

Harvard edX Using Python for Research

utmthesis

The LaTeX class for Universiti Teknologi Malaysia (UTM) thesis.

various-attention-mechanisms

This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc

vehicle_tracking

OpenCV 3 & Keras implementation of vehicle tracking with video data.

vesc-controller

vgg

YOLO Keras(Python) Implementation

vgg16-in-keras

Implementation of VGG16 architecture in Keras

video-classification-cnn-and-lstm-

To classify video into various classes using keras library with tensorflow as back-end.

video-expression-recognition

Recognising expression/emotion of unique faces in a video

violajonescascades

xml files for OpenCV designed for different face related tasks such as head and shoulders , eye , mouth and nose detection. A couple of python sample codes are included. Observe the license included in each xml file.

violencedetection_cnnlstm

Learning to Detect Violent Videos using Convolution LSTM (Keras + tensorflow)

vision

Datasets, Transforms and Models specific to Computer Vision

visual-qa

Keras-based LSTM/CNN models for Visual Question Answering

visualising-facial-expression-changes

In order to complete the process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted

visualization-segmentation-process

Segmentation visualization, keras, augmentation, fine tuning

vpilot

Scripts and tools to easily communicate with DeepGTAV. In the future a self-driving agent will be implemented.

vr-facial-expressions-classification

Facial emotions classification for Virtual Reality Modeling module at Kaunas University of Technology