NamSor API v2 Python SDK - classify personal names accurately by gender, country of origin, or ethnicity.
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Updated
Aug 23, 2023 - Python
NamSor API v2 Python SDK - classify personal names accurately by gender, country of origin, or ethnicity.
A simple demo of gender classifier by using dlib face recognition model as a feature extractor
Deep learning, Face detection, CNN, Tensorflow, Keras, OpenCV, Python crawler
INA's library with pretrained models for gender and age prediction from faces.
NamSor API v2 R SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Guess gender from indian names.
Classify names into male/female using Neural Nets
Gender prediction using mobilenet_V2 finetuned on datasets of celebrities, lamoda, wildberries photos.
The code uses the scikit-learn machine learning library to train a decision tree on a small dataset of body metrics (height, width, and shoe size) labeled male or female. Then we can predict the gender of someone given a novel set of body metrics.
NamSor command line tools, to append gender, origin, diaspora or us 'race'/ethnicity to a CSV file.
NamSor API v2 Java SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Gender classifier from first name
NamSor API v2 Java SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Gender-classifier-python-module
Your name contains a lot of information. It tells if you're male or female and reveals your nationality. This project splits a name or email address into the first and last name and tells if a name is male or female and what the possible nationality is. Additionally this project can generate fake names and extract names from any given text.
Your name contains a lot of information. It tells if you're male or female and reveals your nationality. This project splits a name or email address into the first and last name and tells if a name is male or female and what the possible nationality is. Additionally this project can generate fake names and extract names from any given text.
Implementing gender recognition based on first 14 MFCC coefficients, pitch period, short time energy and spectral centroid
A Project based on CNN, For image gender prediction, With GUI made from Tkinter
Using a machine learning model, classify Georgian names to their corresponding genders.
Applying the Naive Bayes Classifier model to a dataset of art house Sotheby's auction prices, based on the art movement and gender of the artist with the aim to see how these variables could help predict the sale value of the artwork
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