adience-dataset

There are 1 repository under adience-dataset topic.

• dandynaufaldi / Agendernet

  Age and Gender Prediction

  age-predictiongender-predictionimdb-wikiadience-datasetkalman-filtersort
  Language:Python 71

• cjiang2 / AgeEstimateAdience

  Age and Gender Estimation Using Convolutional Neural Network

  adience-datasetageage-estimationalexnetcnncnn-networkconvolutional-neural-networkspredictiontensorflowvggvgg-16vggface
  Language:Python 46

• fadimanakilci / Age-And-Gender-Prediction

  Age and gender detection using openCV with Deep Learning for image or real time camera.

  deep-learningcaffemodelopencvnumpytensorflowpythonreal-timeconvolutional-neural-networksage-gender-estimationadience-datasetalexnet
  Language:Python 2

• carlosccb / Ordinal-Clasification-with-Residual-Networks

  Undergraduate final project: Ordinal Clasification with Residual Networks for the Adience dataset.

  adience-datasetage-recognitionordinal-classificationordinal-regressionresidual-networksresnet
  Language:Python 1

• dandynaufaldi / Age-Gender-Predictor

  This repo is discontinued, moved to https://github.com/dandynaufaldi/Agendernet

  age-gender-cnnssrnetimdb-wikiadience-datasetinsightfacedex
  Language:Jupyter Notebook 1

• g-wtham / realtime-gender-classification

  A gender classification system capable of accurately predicting the gender of individuals from facial images, using pre-trained models

  adience-datasetbinary-classificationcaffemodelgender-classificationrealtime
  Language:Jupyter Notebook 1

• Yuxinn-J / gender-classification-Adience

  [CZ4042 AY2022S1] Gender Classification using Adience Benchmark Dataset with TensorFlow

  adience-datasetdeep-learninggender-classificationntu-scsetensorflow
  Language:Jupyter Notebook 1

• kubershahi / ashoka-iml

  Age and Gender Detection: Project Repo for Intro to Machine Learning (IML) course at Ashoka

  adience-datasetageandgenderdetectioncnnconvolutional-neural-networksdeep-learningkerasmachinelearningtensorflow2
  Language:Jupyter Notebook 0

• Vaibhav-Rathod / Age-estimation-by-MultiStream-CNN

  In this project, I used to extract the face feature (Eye, Nose, Eyebrow, and Mouth) using the DLib library and pass each feature to each of the multi-stream CNN layers. After passing Through Multi-stream CNN the output is concatenated and again passes through the CNN model, the output of CNN is estimated age.

  agepython3jupyter-notebookcomputer-visionagepredictorcnnconvolutional-neural-networksdeep-learning-algorithmsdlibdlib-libraryadience-dataset
  Language:Jupyter Notebook 0