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Age detection in Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 935 |
| 2026-07-25 | 935 |
| 2026-07-28 | 935 |
| 2026-07-30 | 935 |
| 2026-08-06 | 935 |
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Rude Carnie: Age and Gender Deep Learning with TensorFlow
==========================================================
## Goal
Do face detection and age and gender classification on pictures
### Name
http://www.someecards.com/news/getting-old/howoldnet-takes-your-picture-and-uses-algorithms-to-guess-your-age-like-a-rude-carnie/
### Currently Supported Models
- _Gil Levi and Tal Hassner, Age and Gender Classification Using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015_
- http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
- https://github.com/GilLevi/AgeGenderDeepLearning
- Inception v3 with fine-tuning
- This will start with an inception v3 checkpoint, and fine-tune for either age or gender detection
### Running
There are several ways to use a pre-existing checkpoint to do age or gender classification. By default, the code will simply assume that the image you provided has a face in it, and will run that image through a multi-pass classification using the corners and center.
The --class_type parameter controls which task, and the --model_dir controls which checkpoint to restore. There are advanced parameters for the checkpoint basename (--checkpoint) and the requested step number if there are multiple checkpoints in the directory (--requested_step)
Here is a run using Age classification on the latest checkpoint in a directory using 12-look (all corners + center + resized, along with flipped versions) averaging:
```
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_1/run-20854 --filename /home/dpressel/Downloads/portraits/prince.jpg
```
You can also tell it to do a single image classification without the corners and center crop. Here is a run using Age classification on the latest checkpoint in a directory, using a single look at the image
```
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/age_test_fold_is_1/run-20854 --filename /home/dpressel/Downloads/portraits/prince.jpg --single_look
```
Here is a version using gender, where we restore the checkpoint from a specific step:
```
$ python guess.py --model_dir /home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/gen_test_fold_is_0/run-31376 --class_type gender --requested_step 9999 --filename /home/dpressel/Downloads/portraits/prince.jpg
```
#### Face Detection
If you have an image with one or more frontal faces, you can run a face-detector upfront, and each detected face will be chipped out and run through classification individually. A variety of face detectors are supported including OpenCV, dlib and YOLO
OpenCV:
```
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename /home/dpressel/Downloads/portraits/p_and_d.jpg --face_detection_model /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml
```
To use dlib, you will need to install it and grab down the model:
```
wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
bunzip2 bunzip2 shape_predictor_68_face_landmarks.dat.bz2
pip install dlib
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename ~/Downloads/portraits/halloween15.jpg --face_detection_type dlib --face_detection_model shape_predictor_68_face_landmarks.dat
```
YOLO tiny:
```
python guess.py --model_type inception --model_dir /data/xdata/rude-carnie/checkpoints/age/inception/22801 --filename /home/dpressel/Downloads/portraits/p_and_d.jpg --face_detection_model weights/YOLO_tiny.ckpt --face_detection_type yolo_tiny
```
If you want to run YOLO, get the tiny checkpoint from here
https://github.com/gliese581gg/YOLO_tensorflow/
The YOLO detection code is based heavily on:
https://github.com/gliese581gg/YOLO_tensorflow/blob/master/Excerpt of 12,092 characters
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matched fp:ff97fdaeb97138e4, topic:tensorflow