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Exploring the Limits of Compact Model for Age Estimation. CVPR2019

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C3AE_Age_Estimation

Introduction

C3AE: Exploring the Limits of Compact Model for Age Estimation

This repo is organized as follows:

C3AE_Age_Estimation/
    |->examples
    |->models
    |->prepare_data
    |->data
    |   |->img_list
    |   |->dataset
    |->tf_records
    |->ckpt
    |->tools

Requirements

  1. tensorflow-gpu==1.12.0 (I only test on tensorflow 1.12.0)
  2. python==3.4.3
  3. numpy
  4. easydict
  5. opencv==3.4.1
  6. Python packages might missing. pls fix it according to the error message.

Installation, Prepare data, Training

Installation

  1. Clone the C3AE_Age_Estimation repository, and we'll call the directory that you cloned C3AE_Age_Estimation as ${C3AE_Age_Estimation_ROOT}.
git clone https://github.com/vicwer/C3AE_Age_Estimation.git
  1. Create data, tf_records and ckpt directory.
cd ${C3AE_Age_Estimation_ROOT};
mkdir ckpt
mkdir tf_records
mkdir data
cd data
mkdir img_list
mkdir train_list
mkdir dataset

Prepare data

data should be organized as follows:

data/
    |->img_list/img_list.txt
    |->train_list/train.txt
    |->dataset/*.png
  1. Download dataset: IMDB-WIKI, Morph II, FG-NET

  2. Generate img_list.txt formatted as "img_path age"

  3. Generate train.txt formatted as "img_path age_label age_Yn_vector"

  4. Generate tf_records:

cd prepare_data
python3 gen_tf_records_fast_to_uint8.py

Training

I provide common used config.py in ${C3AE_Age_Estimation_ROOT}, which can set hyperparameters.

e.g.

cd ${C3AE_Age_Estimation_ROOT}
vim config.py
cfg.train.num_gpus = {your gpu nums}
etc.

cd ${C3AE_Age_Estimation_ROOT}/examples/
python3 multi_gpus_train.py

TODO:

test.py
tools.py
pre-train model
...

GOOD LUCK...

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Exploring the Limits of Compact Model for Age Estimation. CVPR2019

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