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https://github.com/vermavinay982/age-gender-classifier-vggface

Age prediction by classification instead of regression to find age specific features. Trained on 22k images scraped from Wikipedia.
https://github.com/vermavinay982/age-gender-classifier-vggface

computer-vision deep-learning keras machine-learning

Last synced: 9 days ago
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Age prediction by classification instead of regression to find age specific features. Trained on 22k images scraped from Wikipedia.

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README 

        
## :camera: Age and Gender via Transfer Learning - (on IMDB/ Wiki Dataset) 

This model is using *classification* approach. Trained on 22k images scrapped from Wikipedia. IMDB dataset is also attached and can be used similarly.



## Motivation

General age prediction models uses regression based approach, that is sometimes not so accurate. Using the *classification* approach to find the age by not only using the max argument as we do. Instead, Taking consideration all the prediction values to predict apparent age of the person.



Similar to *Age prediction* - *Gender Prediction* was done by creating binary layer.



## Build status

The model architecture is ready to be used for development and deployment weights are released.



The original work consumed face pictures collected from IMDB (7 GB) and Wikipedia (1 GB). You can find these data sets [here](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/). In this post, I will just consume wiki data source to develop solution fast. You should download faces only files.



## Code style



[![js-standard-style](https://img.shields.io/badge/code%20style-standard-brightgreen.svg?style=flat)](https://github.com/feross/standard)

 

## Screenshots

Include logo/demo screenshot etc.



![](assets/gender_result.png)



## Classification Separation between classes (age 1 to 100)

---

### After 5 epoch

![](assets/initial.png)

### After 100 epoch

![](assets/mid.png)

### After 250 epoch

![](assets/final.png)



## Tech/framework used

Built with

- Tensorflow 2.3.1

- Keras

- Numpy



## Features

Results are very satisfactory even though it does not have a good perspective. Marlon Brando was 48 and Al Pacino was 32 in Godfather Part I.



## Code Example

Researchers develop an age prediction approach and convert classification task to regression. They propose that you should multiply each softmax out with its label.



This is done faster using Numpy.



```python

# Multiclass prediction

predictions = age_model.predict(test_x)

 

# Multiplying the weights of each prediction to class and summing it up

output_indexes = np.array([i for i in range(0, 101)])

actual_predictions = np.sum(predictions * output_indexes, axis = 1)

```



## Installation

- Install the requirements `pip install -r requirements.txt`

- Train the model or Download pretrained weights

- Run the evaluation on the image data by passing the path



## How to use?

- Download dataset and clean it - using `data_loading_cleaning.ipynb` notebook

- Train the model which you are willing to use

- Evaluation script of the same model is there to infer your models



## Contribute

You can for the repository - create a pull request after making changes or can drop the issue by creating a new issue. It would be helpful for the community.



## Credits

Sefik's Blog Post inspired me to build this project



https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/



## Dataset Reference



```

@InProceedings{Rothe-ICCVW-2015,

  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},

  title = {DEX: Deep EXpectation of apparent age from a single image},

  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},

  year = {2015},

  month = {December},

}

```