├── AgeGenderDemo.ipynb ├── DataPreparationCode ├── create_lmdb_test_fold_0.sh ├── create_lmdb_test_fold_1.sh ├── create_lmdb_test_fold_2.sh ├── create_lmdb_test_fold_3.sh ├── create_lmdb_test_fold_4.sh ├── create_train_val_txt_files.py ├── make_mean_imag_test_fold_is_0.sh ├── make_mean_imag_test_fold_is_1.sh ├── make_mean_imag_test_fold_is_2.sh ├── make_mean_imag_test_fold_is_3.sh └── make_mean_imag_test_fold_is_4.sh ├── EmotiW_Demo.ipynb ├── Folds ├── original_txt_files │ ├── fold_0_data.txt │ ├── fold_1_data.txt │ ├── fold_2_data.txt │ ├── fold_3_data.txt │ └── fold_4_data.txt └── train_val_txt_files_per_fold │ ├── test_fold_is_0 │ ├── age_test.txt │ ├── age_train.txt │ ├── age_train_subset.txt │ ├── age_val.txt │ ├── gender_test.txt │ ├── gender_train.txt │ ├── gender_train_subset.txt │ └── gender_val.txt │ ├── test_fold_is_1 │ ├── age_test.txt │ ├── age_train.txt │ ├── age_train_subset.txt │ ├── age_val.txt │ ├── gender_test.txt │ ├── gender_train.txt │ ├── gender_train_subset.txt │ └── gender_val.txt │ ├── test_fold_is_2 │ ├── age_test.txt │ ├── age_train.txt │ ├── age_train_subset.txt │ ├── age_val.txt │ ├── gender_test.txt │ ├── gender_train.txt │ ├── gender_train_subset.txt │ └── gender_val.txt │ ├── test_fold_is_3 │ ├── age_test.txt │ ├── age_train.txt │ ├── age_train_subset.txt │ ├── age_val.txt │ ├── gender_test.txt │ ├── gender_train.txt │ ├── gender_train_subset.txt │ └── gender_val.txt │ └── test_fold_is_4 │ ├── age_test.txt │ ├── age_train.txt │ ├── age_train_subset.txt │ ├── age_val.txt │ ├── gender_test.txt │ ├── gender_train.txt │ ├── gender_train_subset.txt │ └── gender_val.txt ├── README.md ├── age_net_definitions ├── deploy.prototxt ├── solver_test_fold_is_0.prototxt ├── solver_test_fold_is_1.prototxt ├── solver_test_fold_is_2.prototxt ├── solver_test_fold_is_3.prototxt ├── solver_test_fold_is_4.prototxt ├── train_val_test_fold_is_0.prototxt ├── train_val_test_fold_is_1.prototxt ├── train_val_test_fold_is_2.prototxt ├── train_val_test_fold_is_3.prototxt └── train_val_test_fold_is_4.prototxt ├── gender_net_definitions ├── deploy.prototxt ├── solver_test_fold_is_0.prototxt ├── solver_test_fold_is_1.prototxt ├── solver_test_fold_is_2.prototxt ├── solver_test_fold_is_3.prototxt ├── solver_test_fold_is_4.prototxt ├── train_val_test_fold_is_0.prototxt ├── train_val_test_fold_is_1.prototxt ├── train_val_test_fold_is_2.prototxt ├── train_val_test_fold_is_3.prototxt └── train_val_test_fold_is_4.prototxt └── models ├── age_net.caffemodel ├── gender_net.caffemodel └── mean.binaryproto /DataPreparationCode/create_lmdb_test_fold_0.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/DATA/aligned/ 3 | DEF_FILES=/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold/test_fold_is_0 4 | OUT=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0 5 | 6 | # Set RESIZE=true to resize the images to 256x256. Leave as false if images have 7 | # already been resized using another tool. 8 | RESIZE=true 9 | if $RESIZE; then 10 | RESIZE_HEIGHT=256 11 | RESIZE_WIDTH=256 12 | else 13 | RESIZE_HEIGHT=0 14 | RESIZE_WIDTH=0 15 | fi 16 | 17 | 18 | 19 | 20 | echo "Creating train leveldb..." 21 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_train.txt $OUT/age_train_lmdb 22 | 23 | echo "Creating train subset leveldb..." 24 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_test.txt $OUT/age_test_lmdb 25 | 26 | echo "Creating val leveldb..." 27 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_val.txt $OUT/age_val_lmdb 28 | 29 | 30 | 31 | echo "Creating train leveldb..." 32 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_train.txt $OUT/gender_train_lmdb 33 | 34 | echo "Creating train subset leveldb..." 35 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_test.txt $OUT/gender_test_lmdb 36 | 37 | echo "Creating val leveldb..." 38 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_val.txt $OUT/gender_val_lmdb 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /DataPreparationCode/create_lmdb_test_fold_1.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/DATA/aligned/ 3 | DEF_FILES=/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold/test_fold_is_1 4 | OUT=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1 5 | 6 | # Set RESIZE=true to resize the images to 256x256. Leave as false if images have 7 | # already been resized using another tool. 8 | RESIZE=true 9 | if $RESIZE; then 10 | RESIZE_HEIGHT=256 11 | RESIZE_WIDTH=256 12 | else 13 | RESIZE_HEIGHT=0 14 | RESIZE_WIDTH=0 15 | fi 16 | 17 | 18 | 19 | 20 | echo "Creating train leveldb..." 21 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_train.txt $OUT/age_train_lmdb 22 | 23 | echo "Creating train subset leveldb..." 24 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_test.txt $OUT/age_test_lmdb 25 | 26 | echo "Creating val leveldb..." 27 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_val.txt $OUT/age_val_lmdb 28 | 29 | 30 | 31 | echo "Creating train leveldb..." 32 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_train.txt $OUT/gender_train_lmdb 33 | 34 | echo "Creating train subset leveldb..." 35 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_test.txt $OUT/gender_test_lmdb 36 | 37 | echo "Creating val leveldb..." 38 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_val.txt $OUT/gender_val_lmdb 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /DataPreparationCode/create_lmdb_test_fold_2.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/DATA/aligned/ 3 | DEF_FILES=/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold/test_fold_is_2 4 | OUT=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2 5 | 6 | # Set RESIZE=true to resize the images to 256x256. Leave as false if images have 7 | # already been resized using another tool. 8 | RESIZE=true 9 | if $RESIZE; then 10 | RESIZE_HEIGHT=256 11 | RESIZE_WIDTH=256 12 | else 13 | RESIZE_HEIGHT=0 14 | RESIZE_WIDTH=0 15 | fi 16 | 17 | 18 | 19 | 20 | echo "Creating train leveldb..." 21 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_train.txt $OUT/age_train_lmdb 22 | 23 | echo "Creating train subset leveldb..." 24 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_test.txt $OUT/age_test_lmdb 25 | 26 | echo "Creating val leveldb..." 27 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_val.txt $OUT/age_val_lmdb 28 | 29 | 30 | 31 | echo "Creating train leveldb..." 32 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_train.txt $OUT/gender_train_lmdb 33 | 34 | echo "Creating train subset leveldb..." 35 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_test.txt $OUT/gender_test_lmdb 36 | 37 | echo "Creating val leveldb..." 38 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_val.txt $OUT/gender_val_lmdb 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /DataPreparationCode/create_lmdb_test_fold_3.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/DATA/aligned/ 3 | DEF_FILES=/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold/test_fold_is_3 4 | OUT=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3 5 | 6 | # Set RESIZE=true to resize the images to 256x256. Leave as false if images have 7 | # already been resized using another tool. 8 | RESIZE=true 9 | if $RESIZE; then 10 | RESIZE_HEIGHT=256 11 | RESIZE_WIDTH=256 12 | else 13 | RESIZE_HEIGHT=0 14 | RESIZE_WIDTH=0 15 | fi 16 | 17 | 18 | 19 | 20 | echo "Creating train leveldb..." 21 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_train.txt $OUT/age_train_lmdb 22 | 23 | echo "Creating train subset leveldb..." 24 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_test.txt $OUT/age_test_lmdb 25 | 26 | echo "Creating val leveldb..." 27 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_val.txt $OUT/age_val_lmdb 28 | 29 | 30 | 31 | echo "Creating train leveldb..." 32 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_train.txt $OUT/gender_train_lmdb 33 | 34 | echo "Creating train subset leveldb..." 35 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_test.txt $OUT/gender_test_lmdb 36 | 37 | echo "Creating val leveldb..." 38 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_val.txt $OUT/gender_val_lmdb 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /DataPreparationCode/create_lmdb_test_fold_4.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/DATA/aligned/ 3 | DEF_FILES=/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold/test_fold_is_4 4 | OUT=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_4 5 | 6 | # Set RESIZE=true to resize the images to 256x256. Leave as false if images have 7 | # already been resized using another tool. 8 | RESIZE=true 9 | if $RESIZE; then 10 | RESIZE_HEIGHT=256 11 | RESIZE_WIDTH=256 12 | else 13 | RESIZE_HEIGHT=0 14 | RESIZE_WIDTH=0 15 | fi 16 | 17 | 18 | 19 | 20 | echo "Creating train leveldb..." 21 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_train.txt $OUT/age_train_lmdb 22 | 23 | echo "Creating train subset leveldb..." 24 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_test.txt $OUT/age_test_lmdb 25 | 26 | echo "Creating val leveldb..." 27 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/age_val.txt $OUT/age_val_lmdb 28 | 29 | 30 | 31 | echo "Creating train leveldb..." 32 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_train.txt $OUT/gender_train_lmdb 33 | 34 | echo "Creating train subset leveldb..." 35 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_test.txt $OUT/gender_test_lmdb 36 | 37 | echo "Creating val leveldb..." 38 | GLOG_logtostderr=1 $TOOLS/convert_imageset.bin --resize_height=$RESIZE_HEIGHT --resize_width=$RESIZE_WIDTH --shuffle $DATA $DEF_FILES/gender_val.txt $OUT/gender_val_lmdb 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /DataPreparationCode/create_train_val_txt_files.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | DATA_DIR='/home/ubuntu/AdienceFaces/DATA/aligned' 4 | original_folds_dir='/home/ubuntu/AdienceFaces/folds/original_txt_files' 5 | out_files_dir='/home/ubuntu/AdienceFaces/folds/train_val_txt_files_per_fold' 6 | 7 | age_list=['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)'] 8 | gender_list=['m','f'] 9 | #note that since there's a bug in the txt files - (25 32) == (25 23) 10 | 11 | 12 | for cur_test_fold_ind in range(5): 13 | cur_fold_out_foldername='test_fold_is_{0}'.format(cur_test_fold_ind) 14 | cur_fold_out_foldername=os.path.join(out_files_dir,cur_fold_out_foldername) 15 | 16 | if not os.path.exists(cur_fold_out_foldername): 17 | os.mkdir(cur_fold_out_foldername) 18 | 19 | 20 | #test files 21 | cur_test_fold_filename='fold_{0}_data.txt'.format(cur_test_fold_ind) 22 | cur_test_fold_filename=os.path.join(original_folds_dir,cur_test_fold_filename) 23 | 24 | with open(cur_test_fold_filename) as f: 25 | def_lines=f.readlines() 26 | 27 | def_lines.pop(0) 28 | 29 | full_test_list=[] 30 | for def_line in def_lines: 31 | def_dic={} 32 | 33 | subject_dir=def_line.split('\t')[0] 34 | image_subject=def_line.split('\t')[2] 35 | 36 | image_name='landmark_aligned_face.{0}.{1}'.format(image_subject,def_line.split('\t')[1]) 37 | 38 | image_age=def_line.split('\t')[3] 39 | 40 | 41 | if image_age=='(25 23)': 42 | image_age='(25 32)' 43 | 44 | 45 | image_gender=def_line.split('\t')[4] 46 | 47 | 48 | def_dic['subject_dir']=subject_dir 49 | def_dic['image_name']=image_name 50 | def_dic['image_subject']=image_subject 51 | def_dic['image_age']=image_age 52 | def_dic['image_gender']=image_gender 53 | 54 | full_test_list.append(def_dic) 55 | 56 | 57 | images_num=len(full_test_list) 58 | indices=random.sample(set(range(0,images_num)), images_num) 59 | 60 | 61 | #creating text.txt file 62 | age_test_txt_filename=os.path.join(cur_fold_out_foldername,'age_test.txt') 63 | gender_test_txt_filename=os.path.join(cur_fold_out_foldername,'gender_test.txt') 64 | 65 | for f in [age_test_txt_filename,gender_test_txt_filename]: 66 | if os.path.exists(f): 67 | os.remove(f) 68 | 69 | age_test_txt_file=open(age_test_txt_filename,'w+') 70 | gender_test_txt_file=open(gender_test_txt_filename,'w+') 71 | 72 | 73 | for ind in indices: 74 | subject_dir=full_test_list[ind]['subject_dir'] 75 | image_name=full_test_list[ind]['image_name'] 76 | image_age=full_test_list[ind]['image_age'] 77 | image_gender=full_test_list[ind]['image_gender'] 78 | image_subject=full_test_list[ind]['image_subject'] 79 | 80 | if image_age in age_list: 81 | image_age_index=age_list.index(image_age) 82 | s='{0}/{1} {2}\n'.format(subject_dir,image_name,image_age_index) 83 | age_test_txt_file.write(s) 84 | 85 | if image_gender in gender_list: 86 | image_gender_index=gender_list.index(image_gender) 87 | s='{0}/{1} {2}\n'.format(subject_dir,image_name,image_gender_index) 88 | gender_test_txt_file.write(s) 89 | 90 | 91 | for f in [age_test_txt_file,gender_test_txt_file]: 92 | f.close() 93 | 94 | 95 | 96 | full_train_list=[] 97 | train_folds_indices=list(set(range(5)) - set([cur_test_fold_ind])) 98 | 99 | for train_fold_ind in train_folds_indices: 100 | 101 | #test files 102 | cur_train_fold_filename='fold_{0}_data.txt'.format(train_fold_ind) 103 | cur_train_fold_filename=os.path.join(original_folds_dir,cur_train_fold_filename) 104 | 105 | with open(cur_train_fold_filename) as f: 106 | def_lines=f.readlines() 107 | 108 | def_lines.pop(0) 109 | 110 | for def_line in def_lines: 111 | def_dic={} 112 | 113 | subject_dir=def_line.split('\t')[0] 114 | image_subject=def_line.split('\t')[2] 115 | image_name='landmark_aligned_face.{0}.{1}'.format(image_subject,def_line.split('\t')[1]) 116 | 117 | image_age=def_line.split('\t')[3] 118 | 119 | if image_age=='(25 23)': 120 | image_age='(25 32)' 121 | 122 | 123 | image_gender=def_line.split('\t')[4] 124 | 125 | 126 | def_dic['subject_dir']=subject_dir 127 | def_dic['image_name']=image_name 128 | def_dic['image_subject']=image_subject 129 | def_dic['image_age']=image_age 130 | def_dic['image_gender']=image_gender 131 | 132 | full_train_list.append(def_dic) 133 | 134 | 135 | 136 | images_num=len(full_train_list) 137 | indices=random.sample(set(range(0,images_num)), images_num) 138 | 139 | val_indices=indices[:images_num/10] 140 | train_indices=indices[(images_num/10) + 1:] 141 | train_subset_indices=indices[(images_num/10) + 1: 2* (images_num/10)] 142 | #train files 143 | 144 | cases=['val','train','train_subset'] 145 | 146 | for case,indices in zip(cases,[val_indices,train_indices,train_subset_indices]): 147 | 148 | age_txt_filename=os.path.join(cur_fold_out_foldername,'age_{0}.txt'.format(case)) 149 | gender_txt_filename=os.path.join(cur_fold_out_foldername,'gender_{0}.txt'.format(case)) 150 | 151 | for f in [age_txt_filename,gender_txt_filename]: 152 | if os.path.exists(f): 153 | os.remove(f) 154 | 155 | age_txt_file=open(age_txt_filename,'w+') 156 | gender_txt_file=open(gender_txt_filename,'w+') 157 | 158 | 159 | 160 | for ind in indices: 161 | subject_dir=full_train_list[ind]['subject_dir'] 162 | image_name=full_train_list[ind]['image_name'] 163 | image_age=full_train_list[ind]['image_age'] 164 | image_gender=full_train_list[ind]['image_gender'] 165 | image_subject=full_train_list[ind]['image_subject'] 166 | if image_age in age_list: 167 | image_age_index=age_list.index(image_age) 168 | s='{0}/{1} {2}\n'.format(subject_dir,image_name,image_age_index) 169 | age_txt_file.write(s) 170 | 171 | if image_gender in gender_list: 172 | image_gender_index=gender_list.index(image_gender) 173 | s='{0}/{1} {2}\n'.format(subject_dir,image_name,image_gender_index) 174 | gender_txt_file.write(s) 175 | 176 | 177 | for f in [age_txt_file,gender_txt_file]: 178 | f.close() 179 | 180 | 181 | -------------------------------------------------------------------------------- /DataPreparationCode/make_mean_imag_test_fold_is_0.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0/gender_train_lmdb 3 | OUT=/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_0 4 | 5 | $TOOLS/compute_image_mean.bin $DATA $OUT/mean.binaryproto 6 | 7 | -------------------------------------------------------------------------------- /DataPreparationCode/make_mean_imag_test_fold_is_1.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1/gender_train_lmdb 3 | OUT=/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_1 4 | 5 | $TOOLS/compute_image_mean.bin $DATA $OUT/mean.binaryproto 6 | 7 | -------------------------------------------------------------------------------- /DataPreparationCode/make_mean_imag_test_fold_is_2.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2/gender_train_lmdb 3 | OUT=/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_2 4 | 5 | $TOOLS/compute_image_mean.bin $DATA $OUT/mean.binaryproto 6 | 7 | -------------------------------------------------------------------------------- /DataPreparationCode/make_mean_imag_test_fold_is_3.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | DATA=/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3/gender_train_lmdb 3 | OUT=/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_3 4 | 5 | $TOOLS/compute_image_mean.bin $DATA $OUT/mean.binaryproto 6 | 7 | -------------------------------------------------------------------------------- /DataPreparationCode/make_mean_imag_test_fold_is_4.sh: -------------------------------------------------------------------------------- 1 | TOOLS=/home/ubuntu/repositories/caffe/build/tools 2 | 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1285 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AgeGenderDeepLearning 2 | 3 | ## Description 4 | 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: 5 | 6 | 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 7 | 8 | Project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/ 9 | 10 | 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. 11 | 12 |

13 | We have also uploaded an ipython notetebook with example usage of our emotion classification networks from our paper: 14 | 15 | 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 16 | 17 | If you find our models or code useful, please add suitable reference to our paper in your work. 18 | 19 | Also see TensorFlow implementation of our work by Rude Carnie: https://github.com/dpressel/rude-carnie 20 | 21 | 22 | --- 23 | Copyright 2015, Gil Levi and Tal Hassner 24 | 25 | 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. 26 | -------------------------------------------------------------------------------- /age_net_definitions/deploy.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | input: "data" 3 | input_dim: 1 4 | input_dim: 3 5 | input_dim: 227 6 | input_dim: 227 7 | layers { 8 | name: "conv1" 9 | type: CONVOLUTION 10 | bottom: "data" 11 | top: "conv1" 12 | convolution_param { 13 | num_output: 96 14 | kernel_size: 7 15 | stride: 4 16 | } 17 | } 18 | layers { 19 | name: "relu1" 20 | type: RELU 21 | bottom: "conv1" 22 | top: "conv1" 23 | } 24 | layers { 25 | name: "pool1" 26 | type: POOLING 27 | bottom: "conv1" 28 | top: "pool1" 29 | pooling_param { 30 | pool: MAX 31 | kernel_size: 3 32 | stride: 2 33 | } 34 | } 35 | layers { 36 | name: "norm1" 37 | type: LRN 38 | bottom: "pool1" 39 | top: "norm1" 40 | lrn_param { 41 | local_size: 5 42 | alpha: 0.0001 43 | beta: 0.75 44 | } 45 | } 46 | layers { 47 | name: "conv2" 48 | type: CONVOLUTION 49 | bottom: "norm1" 50 | top: "conv2" 51 | convolution_param { 52 | num_output: 256 53 | pad: 2 54 | kernel_size: 5 55 | } 56 | } 57 | layers { 58 | name: "relu2" 59 | type: RELU 60 | bottom: "conv2" 61 | top: "conv2" 62 | } 63 | layers { 64 | name: "pool2" 65 | type: POOLING 66 | bottom: "conv2" 67 | top: "pool2" 68 | pooling_param { 69 | pool: MAX 70 | kernel_size: 3 71 | stride: 2 72 | } 73 | } 74 | layers { 75 | name: "norm2" 76 | type: LRN 77 | bottom: "pool2" 78 | top: "norm2" 79 | lrn_param { 80 | local_size: 5 81 | alpha: 0.0001 82 | beta: 0.75 83 | } 84 | } 85 | layers { 86 | name: "conv3" 87 | type: CONVOLUTION 88 | bottom: "norm2" 89 | top: "conv3" 90 | convolution_param { 91 | num_output: 384 92 | pad: 1 93 | kernel_size: 3 94 | } 95 | } 96 | layers{ 97 | name: "relu3" 98 | type: RELU 99 | bottom: "conv3" 100 | top: "conv3" 101 | } 102 | layers { 103 | name: "pool5" 104 | type: POOLING 105 | bottom: "conv3" 106 | top: "pool5" 107 | pooling_param { 108 | pool: MAX 109 | kernel_size: 3 110 | stride: 2 111 | } 112 | } 113 | layers { 114 | name: "fc6" 115 | type: INNER_PRODUCT 116 | bottom: "pool5" 117 | top: "fc6" 118 | inner_product_param { 119 | num_output: 512 120 | } 121 | } 122 | layers { 123 | name: "relu6" 124 | type: RELU 125 | bottom: "fc6" 126 | top: "fc6" 127 | } 128 | layers { 129 | name: "drop6" 130 | type: DROPOUT 131 | bottom: "fc6" 132 | top: "fc6" 133 | dropout_param { 134 | dropout_ratio: 0.5 135 | } 136 | } 137 | layers { 138 | name: "fc7" 139 | type: INNER_PRODUCT 140 | bottom: "fc6" 141 | top: "fc7" 142 | inner_product_param { 143 | num_output: 512 144 | } 145 | } 146 | layers { 147 | name: "relu7" 148 | type: RELU 149 | bottom: "fc7" 150 | top: "fc7" 151 | } 152 | layers { 153 | name: "drop7" 154 | type: DROPOUT 155 | bottom: "fc7" 156 | top: "fc7" 157 | dropout_param { 158 | dropout_ratio: 0.5 159 | } 160 | } 161 | layers { 162 | name: "fc8" 163 | type: INNER_PRODUCT 164 | bottom: "fc7" 165 | top: "fc8" 166 | inner_product_param { 167 | num_output: 8 168 | } 169 | } 170 | layers { 171 | name: "prob" 172 | type: SOFTMAX 173 | bottom: "fc8" 174 | top: "prob" 175 | } 176 | -------------------------------------------------------------------------------- /age_net_definitions/solver_test_fold_is_0.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_0.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /age_net_definitions/solver_test_fold_is_1.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_1.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /age_net_definitions/solver_test_fold_is_2.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_2.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /age_net_definitions/solver_test_fold_is_3.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_3.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /age_net_definitions/solver_test_fold_is_4.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_4.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /age_net_definitions/train_val_test_fold_is_0.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0/age_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_0/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0/age_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_0/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 8 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /age_net_definitions/train_val_test_fold_is_1.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1/age_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_1/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1/age_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_1/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 8 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /age_net_definitions/train_val_test_fold_is_2.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2/age_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_2/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2/age_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_2/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 8 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /age_net_definitions/train_val_test_fold_is_3.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3/age_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_3/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3/age_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_3/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 8 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /age_net_definitions/train_val_test_fold_is_4.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_4/age_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_4/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_4/age_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_4/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 8 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /gender_net_definitions/deploy.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | input: "data" 3 | input_dim: 10 4 | input_dim: 3 5 | input_dim: 227 6 | input_dim: 227 7 | layers { 8 | name: "conv1" 9 | type: CONVOLUTION 10 | bottom: "data" 11 | top: "conv1" 12 | convolution_param { 13 | num_output: 96 14 | kernel_size: 7 15 | stride: 4 16 | } 17 | } 18 | layers { 19 | name: "relu1" 20 | type: RELU 21 | bottom: "conv1" 22 | top: "conv1" 23 | } 24 | layers { 25 | name: "pool1" 26 | type: POOLING 27 | bottom: "conv1" 28 | top: "pool1" 29 | pooling_param { 30 | pool: MAX 31 | kernel_size: 3 32 | stride: 2 33 | } 34 | } 35 | layers { 36 | name: "norm1" 37 | type: LRN 38 | bottom: "pool1" 39 | top: "norm1" 40 | lrn_param { 41 | local_size: 5 42 | alpha: 0.0001 43 | beta: 0.75 44 | } 45 | } 46 | layers { 47 | name: "conv2" 48 | type: CONVOLUTION 49 | bottom: "norm1" 50 | top: "conv2" 51 | convolution_param { 52 | num_output: 256 53 | pad: 2 54 | kernel_size: 5 55 | } 56 | } 57 | layers { 58 | name: "relu2" 59 | type: RELU 60 | bottom: "conv2" 61 | top: "conv2" 62 | } 63 | layers { 64 | name: "pool2" 65 | type: POOLING 66 | bottom: "conv2" 67 | top: "pool2" 68 | pooling_param { 69 | pool: MAX 70 | kernel_size: 3 71 | stride: 2 72 | } 73 | } 74 | layers { 75 | name: "norm2" 76 | type: LRN 77 | bottom: "pool2" 78 | top: "norm2" 79 | lrn_param { 80 | local_size: 5 81 | alpha: 0.0001 82 | beta: 0.75 83 | } 84 | } 85 | layers { 86 | name: "conv3" 87 | type: CONVOLUTION 88 | bottom: "norm2" 89 | top: "conv3" 90 | convolution_param { 91 | num_output: 384 92 | pad: 1 93 | kernel_size: 3 94 | } 95 | } 96 | layers{ 97 | name: "relu3" 98 | type: RELU 99 | bottom: "conv3" 100 | top: "conv3" 101 | } 102 | layers { 103 | name: "pool5" 104 | type: POOLING 105 | bottom: "conv3" 106 | top: "pool5" 107 | pooling_param { 108 | pool: MAX 109 | kernel_size: 3 110 | stride: 2 111 | } 112 | } 113 | layers { 114 | name: "fc6" 115 | type: INNER_PRODUCT 116 | bottom: "pool5" 117 | top: "fc6" 118 | inner_product_param { 119 | num_output: 512 120 | } 121 | } 122 | layers { 123 | name: "relu6" 124 | type: RELU 125 | bottom: "fc6" 126 | top: "fc6" 127 | } 128 | layers { 129 | name: "drop6" 130 | type: DROPOUT 131 | bottom: "fc6" 132 | top: "fc6" 133 | dropout_param { 134 | dropout_ratio: 0.5 135 | } 136 | } 137 | layers { 138 | name: "fc7" 139 | type: INNER_PRODUCT 140 | bottom: "fc6" 141 | top: "fc7" 142 | inner_product_param { 143 | num_output: 512 144 | } 145 | } 146 | layers { 147 | name: "relu7" 148 | type: RELU 149 | bottom: "fc7" 150 | top: "fc7" 151 | } 152 | layers { 153 | name: "drop7" 154 | type: DROPOUT 155 | bottom: "fc7" 156 | top: "fc7" 157 | dropout_param { 158 | dropout_ratio: 0.5 159 | } 160 | } 161 | layers { 162 | name: "fc8" 163 | type: INNER_PRODUCT 164 | bottom: "fc7" 165 | top: "fc8" 166 | inner_product_param { 167 | num_output: 2 168 | } 169 | } 170 | layers { 171 | name: "prob" 172 | type: SOFTMAX 173 | bottom: "fc8" 174 | top: "prob" 175 | } 176 | -------------------------------------------------------------------------------- /gender_net_definitions/solver_test_fold_is_0.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_0.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /gender_net_definitions/solver_test_fold_is_1.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_1.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /gender_net_definitions/solver_test_fold_is_2.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_2.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /gender_net_definitions/solver_test_fold_is_3.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_3.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /gender_net_definitions/solver_test_fold_is_4.prototxt: -------------------------------------------------------------------------------- 1 | net: "train_val_test_fold_is_4.prototxt" 2 | test_iter: 1000 3 | test_interval: 1000 4 | base_lr: 0.001 5 | lr_policy: "step" 6 | gamma: 0.1 7 | stepsize: 10000 8 | display: 20 9 | max_iter: 50000 10 | momentum: 0.9 11 | weight_decay: 0.0005 12 | snapshot: 1000 13 | snapshot_prefix: "caffenet_train" 14 | solver_mode: GPU 15 | -------------------------------------------------------------------------------- /gender_net_definitions/train_val_test_fold_is_0.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0/gender_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_0/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_0/gender_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_0/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 2 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /gender_net_definitions/train_val_test_fold_is_1.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1/gender_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_1/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_1/gender_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_1/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 2 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /gender_net_definitions/train_val_test_fold_is_2.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2/gender_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_2/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_2/gender_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_2/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 2 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /gender_net_definitions/train_val_test_fold_is_3.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3/gender_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_3/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_3/gender_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_3/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 2 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /gender_net_definitions/train_val_test_fold_is_4.prototxt: -------------------------------------------------------------------------------- 1 | name: "CaffeNet" 2 | layers { 3 | name: "data" 4 | type: DATA 5 | top: "data" 6 | top: "label" 7 | data_param { 8 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_4/gender_train_lmdb" 9 | backend: LMDB 10 | batch_size: 50 11 | } 12 | transform_param { 13 | crop_size: 227 14 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_4/mean.binaryproto" 15 | mirror: true 16 | } 17 | include: { phase: TRAIN } 18 | } 19 | layers { 20 | name: "data" 21 | type: DATA 22 | top: "data" 23 | top: "label" 24 | data_param { 25 | source: "/home/ubuntu/AdienceFaces/lmdb/Test_fold_is_4/gender_val_lmdb" 26 | backend: LMDB 27 | batch_size: 50 28 | } 29 | transform_param { 30 | crop_size: 227 31 | mean_file: "/home/ubuntu/AdienceFaces/mean_image/Test_folder_is_4/mean.binaryproto" 32 | mirror: false 33 | } 34 | include: { phase: TEST } 35 | } 36 | layers { 37 | name: "conv1" 38 | type: CONVOLUTION 39 | bottom: "data" 40 | top: "conv1" 41 | blobs_lr: 1 42 | blobs_lr: 2 43 | weight_decay: 1 44 | weight_decay: 0 45 | convolution_param { 46 | num_output: 96 47 | kernel_size: 7 48 | stride: 4 49 | weight_filler { 50 | type: "gaussian" 51 | std: 0.01 52 | } 53 | bias_filler { 54 | type: "constant" 55 | value: 0 56 | } 57 | } 58 | } 59 | layers { 60 | name: "relu1" 61 | type: RELU 62 | bottom: "conv1" 63 | top: "conv1" 64 | } 65 | layers { 66 | name: "pool1" 67 | type: POOLING 68 | bottom: "conv1" 69 | top: "pool1" 70 | pooling_param { 71 | pool: MAX 72 | kernel_size: 3 73 | stride: 2 74 | } 75 | } 76 | layers { 77 | name: "norm1" 78 | type: LRN 79 | bottom: "pool1" 80 | top: "norm1" 81 | lrn_param { 82 | local_size: 5 83 | alpha: 0.0001 84 | beta: 0.75 85 | } 86 | } 87 | layers { 88 | name: "conv2" 89 | type: CONVOLUTION 90 | bottom: "norm1" 91 | top: "conv2" 92 | blobs_lr: 1 93 | blobs_lr: 2 94 | weight_decay: 1 95 | weight_decay: 0 96 | convolution_param { 97 | num_output: 256 98 | pad: 2 99 | kernel_size: 5 100 | weight_filler { 101 | type: "gaussian" 102 | std: 0.01 103 | } 104 | bias_filler { 105 | type: "constant" 106 | value: 1 107 | } 108 | } 109 | } 110 | layers { 111 | name: "relu2" 112 | type: RELU 113 | bottom: "conv2" 114 | top: "conv2" 115 | } 116 | layers { 117 | name: "pool2" 118 | type: POOLING 119 | bottom: "conv2" 120 | top: "pool2" 121 | pooling_param { 122 | pool: MAX 123 | kernel_size: 3 124 | stride: 2 125 | } 126 | } 127 | layers { 128 | name: "norm2" 129 | type: LRN 130 | bottom: "pool2" 131 | top: "norm2" 132 | lrn_param { 133 | local_size: 5 134 | alpha: 0.0001 135 | beta: 0.75 136 | } 137 | } 138 | layers { 139 | name: "conv3" 140 | type: CONVOLUTION 141 | bottom: "norm2" 142 | top: "conv3" 143 | blobs_lr: 1 144 | blobs_lr: 2 145 | weight_decay: 1 146 | weight_decay: 0 147 | convolution_param { 148 | num_output: 384 149 | pad: 1 150 | kernel_size: 3 151 | weight_filler { 152 | type: "gaussian" 153 | std: 0.01 154 | } 155 | bias_filler { 156 | type: "constant" 157 | value: 0 158 | } 159 | } 160 | } 161 | layers{ 162 | name: "relu3" 163 | type: RELU 164 | bottom: "conv3" 165 | top: "conv3" 166 | } 167 | layers { 168 | name: "pool5" 169 | type: POOLING 170 | bottom: "conv3" 171 | top: "pool5" 172 | pooling_param { 173 | pool: MAX 174 | kernel_size: 3 175 | stride: 2 176 | } 177 | } 178 | layers { 179 | name: "fc6" 180 | type: INNER_PRODUCT 181 | bottom: "pool5" 182 | top: "fc6" 183 | blobs_lr: 1 184 | blobs_lr: 2 185 | weight_decay: 1 186 | weight_decay: 0 187 | inner_product_param { 188 | num_output: 512 189 | weight_filler { 190 | type: "gaussian" 191 | std: 0.005 192 | } 193 | bias_filler { 194 | type: "constant" 195 | value: 1 196 | } 197 | } 198 | } 199 | layers { 200 | name: "relu6" 201 | type: RELU 202 | bottom: "fc6" 203 | top: "fc6" 204 | } 205 | layers { 206 | name: "drop6" 207 | type: DROPOUT 208 | bottom: "fc6" 209 | top: "fc6" 210 | dropout_param { 211 | dropout_ratio: 0.5 212 | } 213 | } 214 | layers { 215 | name: "fc7" 216 | type: INNER_PRODUCT 217 | bottom: "fc6" 218 | top: "fc7" 219 | blobs_lr: 1 220 | blobs_lr: 2 221 | weight_decay: 1 222 | weight_decay: 0 223 | inner_product_param { 224 | num_output: 512 225 | weight_filler { 226 | type: "gaussian" 227 | std: 0.005 228 | } 229 | bias_filler { 230 | type: "constant" 231 | value: 1 232 | } 233 | } 234 | } 235 | layers { 236 | name: "relu7" 237 | type: RELU 238 | bottom: "fc7" 239 | top: "fc7" 240 | } 241 | layers { 242 | name: "drop7" 243 | type: DROPOUT 244 | bottom: "fc7" 245 | top: "fc7" 246 | dropout_param { 247 | dropout_ratio: 0.5 248 | } 249 | } 250 | layers { 251 | name: "fc8" 252 | type: INNER_PRODUCT 253 | bottom: "fc7" 254 | top: "fc8" 255 | blobs_lr: 10 256 | blobs_lr: 20 257 | weight_decay: 1 258 | weight_decay: 0 259 | inner_product_param { 260 | num_output: 2 261 | weight_filler { 262 | type: "gaussian" 263 | std: 0.01 264 | } 265 | bias_filler { 266 | type: "constant" 267 | value: 0 268 | } 269 | } 270 | } 271 | layers { 272 | name: "accuracy" 273 | type: ACCURACY 274 | bottom: "fc8" 275 | bottom: "label" 276 | top: "accuracy" 277 | include: { phase: TEST } 278 | } 279 | layers { 280 | name: "loss" 281 | type: SOFTMAX_LOSS 282 | bottom: "fc8" 283 | bottom: "label" 284 | top: "loss" 285 | } 286 | -------------------------------------------------------------------------------- /models/age_net.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GilLevi/AgeGenderDeepLearning/2d591d4663b3faf778377f6b4377285a96262cb5/models/age_net.caffemodel -------------------------------------------------------------------------------- /models/gender_net.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GilLevi/AgeGenderDeepLearning/2d591d4663b3faf778377f6b4377285a96262cb5/models/gender_net.caffemodel -------------------------------------------------------------------------------- /models/mean.binaryproto: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GilLevi/AgeGenderDeepLearning/2d591d4663b3faf778377f6b4377285a96262cb5/models/mean.binaryproto --------------------------------------------------------------------------------