Releases: yayoimizuha/helloproject-blog-image-clawler
Releases · yayoimizuha/helloproject-blog-image-clawler
First Release!
Hello!Projectのメンバーのアメブロから収集した画像を、MediaPipeのBlazeFaceで顔検出&切り出しを行い、VGGFace2で学習させたFaceNetを用いて特徴量を抽出して、マンハッタン距離でtopKアルゴリズムで分類したものです。
2023-01-15 12:13:58.165751.h5の中身(model.summary())
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3 0 []
)]
conv1/7x7_s2 (Conv2D) (None, 112, 112, 64 9408 ['input_1[0][0]']
)
conv1/7x7_s2/bn (BatchNormaliz (None, 112, 112, 64 256 ['conv1/7x7_s2[0][0]']
ation) )
activation (Activation) (None, 112, 112, 64 0 ['conv1/7x7_s2/bn[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 55, 55, 64) 0 ['activation[0][0]']
conv2_1_1x1_reduce (Conv2D) (None, 55, 55, 64) 4096 ['max_pooling2d[0][0]']
conv2_1_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_1_1x1_reduce[0][0]']
rmalization)
activation_1 (Activation) (None, 55, 55, 64) 0 ['conv2_1_1x1_reduce/bn[0][0]']
conv2_1_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_1[0][0]']
conv2_1_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_1_3x3[0][0]']
tion)
activation_2 (Activation) (None, 55, 55, 64) 0 ['conv2_1_3x3/bn[0][0]']
conv2_1_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_2[0][0]']
conv2_1_1x1_proj (Conv2D) (None, 55, 55, 256) 16384 ['max_pooling2d[0][0]']
conv2_1_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_1_1x1_increase[0][0]']
Normalization)
conv2_1_1x1_proj/bn (BatchNorm (None, 55, 55, 256) 1024 ['conv2_1_1x1_proj[0][0]']
alization)
add (Add) (None, 55, 55, 256) 0 ['conv2_1_1x1_increase/bn[0][0]',
'conv2_1_1x1_proj/bn[0][0]']
activation_3 (Activation) (None, 55, 55, 256) 0 ['add[0][0]']
conv2_2_1x1_reduce (Conv2D) (None, 55, 55, 64) 16384 ['activation_3[0][0]']
conv2_2_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_2_1x1_reduce[0][0]']
rmalization)
activation_4 (Activation) (None, 55, 55, 64) 0 ['conv2_2_1x1_reduce/bn[0][0]']
conv2_2_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_4[0][0]']
conv2_2_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_2_3x3[0][0]']
tion)
activation_5 (Activation) (None, 55, 55, 64) 0 ['conv2_2_3x3/bn[0][0]']
conv2_2_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_5[0][0]']
conv2_2_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_2_1x1_increase[0][0]']
Normalization)
add_1 (Add) (None, 55, 55, 256) 0 ['conv2_2_1x1_increase/bn[0][0]',
'activation_3[0][0]']
activation_6 (Activation) (None, 55, 55, 256) 0 ['add_1[0][0]']
conv2_3_1x1_reduce (Conv2D) (None, 55, 55, 64) 16384 ['activation_6[0][0]']
conv2_3_1x1_reduce/bn (BatchNo (None, 55, 55, 64) 256 ['conv2_3_1x1_reduce[0][0]']
rmalization)
activation_7 (Activation) (None, 55, 55, 64) 0 ['conv2_3_1x1_reduce/bn[0][0]']
conv2_3_3x3 (Conv2D) (None, 55, 55, 64) 36864 ['activation_7[0][0]']
conv2_3_3x3/bn (BatchNormaliza (None, 55, 55, 64) 256 ['conv2_3_3x3[0][0]']
tion)
activation_8 (Activation) (None, 55, 55, 64) 0 ['conv2_3_3x3/bn[0][0]']
conv2_3_1x1_increase (Conv2D) (None, 55, 55, 256) 16384 ['activation_8[0][0]']
conv2_3_1x1_increase/bn (Batch (None, 55, 55, 256) 1024 ['conv2_3_1x1_increase[0][0]']
Normalization)
add_2 (Add) (None, 55, 55, 256) 0 ['conv2_3_1x1_increase/bn[0][0]',
'activation_6[0][0]']
activation_9 (Activation) (None, 55, 55, 256) 0 ['add_2[0][0]']
conv3_1_1x1_reduce (Conv2D) (None, 28, 28, 128) 32768 ['activation_9[0][0]']
conv3_1_1x1_reduce/bn (BatchNo (None, 28, 28, 128) 512 ['conv3_1_1x1_reduce[0][0]']
rmalization) ...