# AgeGenderDeepLearning
## Description
The purpose of this repository is to assist readers in reproducing our results on age and gender classification for facial images as described in the following work:
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
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
The code contains the original meta-data files with age and gender labels, a python script for creating prototxt file in order to create the lmdb's for training and shell files for creating the lmdb and mean images.
<br><br>
We have also uploaded an ipython notetebook with example usage of our emotion classification networks from our paper:
Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015
If you find our models or code useful, please add suitable reference to our paper in your work.
Also see TensorFlow implementation of our work by Rude Carnie: https://github.com/dpressel/rude-carnie
---
Copyright 2015, Gil Levi and Tal Hassner
The SOFTWARE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use. It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally be caused through its use.
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收起资源包目录
AgeGenderDeepLearning-master.zip (84个子文件)
AgeGenderDeepLearning-master
models
gender_net.caffemodel 43.53MB
age_net.caffemodel 43.55MB
mean.binaryproto 768KB
EmotiW_Demo.ipynb 3.23MB
Folds
original_txt_files
fold_0_data.txt 356KB
fold_3_data.txt 280KB
fold_2_data.txt 315KB
fold_1_data.txt 298KB
fold_4_data.txt 309KB
train_val_txt_files_per_fold
test_fold_is_3
age_val.txt 99KB
gender_test.txt 231KB
age_test.txt 207KB
gender_train.txt 878KB
age_train_subset.txt 99KB
gender_val.txt 98KB
age_train.txt 887KB
gender_train_subset.txt 97KB
test_fold_is_0
age_val.txt 88KB
gender_test.txt 270KB
age_test.txt 291KB
gender_train.txt 844KB
age_train_subset.txt 89KB
gender_val.txt 92KB
age_train.txt 814KB
gender_train_subset.txt 93KB
test_fold_is_1
age_val.txt 98KB
gender_test.txt 248KB
age_test.txt 212KB
gender_train.txt 862KB
age_train_subset.txt 98KB
gender_val.txt 96KB
age_train.txt 884KB
gender_train_subset.txt 96KB
test_fold_is_2
age_val.txt 98KB
gender_test.txt 219KB
age_test.txt 229KB
gender_train.txt 890KB
age_train_subset.txt 97KB
gender_val.txt 98KB
age_train.txt 866KB
gender_train_subset.txt 100KB
test_fold_is_4
age_val.txt 93KB
gender_test.txt 239KB
age_test.txt 255KB
gender_train.txt 870KB
age_train_subset.txt 94KB
gender_val.txt 97KB
age_train.txt 845KB
gender_train_subset.txt 96KB
age_net_definitions
solver_test_fold_is_4.prototxt 266B
train_val_test_fold_is_1.prototxt 4KB
train_val_test_fold_is_2.prototxt 4KB
solver_test_fold_is_1.prototxt 266B
deploy.prototxt 2KB
train_val_test_fold_is_4.prototxt 4KB
solver_test_fold_is_2.prototxt 266B
solver_test_fold_is_3.prototxt 266B
solver_test_fold_is_0.prototxt 266B
train_val_test_fold_is_3.prototxt 4KB
train_val_test_fold_is_0.prototxt 4KB
DataPreparationCode
create_lmdb_test_fold_3.sh 2KB
create_lmdb_test_fold_1.sh 2KB
make_mean_imag_test_fold_is_2.sh 237B
create_lmdb_test_fold_4.sh 2KB
make_mean_imag_test_fold_is_3.sh 237B
create_train_val_txt_files.py 6KB
make_mean_imag_test_fold_is_0.sh 237B
make_mean_imag_test_fold_is_4.sh 237B
create_lmdb_test_fold_2.sh 2KB
make_mean_imag_test_fold_is_1.sh 237B
create_lmdb_test_fold_0.sh 2KB
gender_net_definitions
solver_test_fold_is_4.prototxt 266B
train_val_test_fold_is_1.prototxt 4KB
train_val_test_fold_is_2.prototxt 4KB
solver_test_fold_is_1.prototxt 266B
deploy.prototxt 2KB
train_val_test_fold_is_4.prototxt 4KB
solver_test_fold_is_2.prototxt 266B
solver_test_fold_is_3.prototxt 266B
solver_test_fold_is_0.prototxt 266B
train_val_test_fold_is_3.prototxt 4KB
train_val_test_fold_is_0.prototxt 4KB
AgeGenderDemo.ipynb 1.37MB
README.md 2KB
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