/age_gender_estimation

Primary LanguagePythonMIT LicenseMIT

Age and Gender estimation based on CNN and TensorFlow

N|Solid

CircleCI

Implementing CNN to estimate age and gender from faces

multi-face-result

DEPENDENCIES

This project has following dependencies and tested under OSX with Python2.7.14. We use virtualenv to play safely with multiple Python versions.

  • tensorflow==1.4
  • dlib==19.7.99
  • cv2
  • matplotlib==2.1.0
  • imutils==0.4.3
  • numpy==1.13.3
  • pandas==0.20.3

HOW TO USE?

One picture demo

In order to test the model in one sigle picture, run

python eval.py --I "./images/roger.png" --M "./models/" --font_scale 1 --thickness 1

Flag --I tells where your picture is.If the text label too small or too large on the picture,you can use a different --font_scale 1 and --thickness 1 to adjust the text size and thickness.

Real time estimation using webcam

web cam

TRAIN YOUR OWN MODELS

Make tfrecords

In order to train your own models,you should first download imdb or wiki dataset,and then extract it under data path,after that,images path should look like

/path/to/project/data/imdb_crop/00/somepictures
/path/to/project/data/imdb_crop/01/somepictures
...
/path/to/project/data/imdb_crop/99/somepictures

Then you can run

python convert_to_records_multiCPU.py --imdb --nworks 8

to convert images to tfrecords.--imdb means using imdb dataset,--nworks 8 means using 8 cpu cores to convert the dataset parallelly.Because we will first detect and align faces in the pictures,which is a time consuming step,so we recommend to use as many cores as possible.Intel E5-2667 v4 and with 32 cores need approximately 50 minutes.

Train model

Once you have converted images to tfrecords,you should have the following path:

/path/to/project/data/train/train-000.tfrecords
...
/path/to/project/data/test/test-000.tfrecords

At present, our deep CNN uses FaceNet architecture,which based on inception-resnet-v1 to extract features.To speed up training,we use the pretrained model's weight from this project and have converted the weight to adapt our model, you can download this converted pretrained facenet weight checkpoint from here or here.Extract it to path models.

/path/to/project/models/checkpoint
/path/to/project/models/model.ckpt-0.data-00000-of-00001
/path/to/project/models/model.ckpt-0.index
/path/to/project/models/model.ckpt-0.meta

NOTE: This step is optional,you can also train your model from scratch. To start training,run

python train.py --lr 1e-3 --weight_decay 1e-5 --epoch 6 --batch_size 128 --keep_prob 0.8 --cuda

NOTE: Using the flag --cuda will train the model with GPU.

Using tensorboard to visualize learning

tensorboard --logdir=./train_log

train log

Test your model

You can test all your trained models on testset through

python test.py --images "./data/test" --model_path "./models" --batch_size 128 --choose_best --cuda

Flag --cuda means using GPU when testing.--choose_best means testing all trained models and return the best one.If you just want to test the latest saved model,without this flag.

python test.py --images "./data/test" --model_path "./models" --batch_size 128 --cuda

MIT License

Copyright (c) [2019] [UNX Digital]