還是得從下圖說起，之前一直沒實際搞清楚。
ssd的網路結構流程如下圖所示：
ssd總共11個block，相比較於之前的vgg16，改變了第5個block的第4層，第6、7、8卷積層全部去掉，分別增加了紅框、黑框、黃框、藍框。
tensorflow**如下：
with tf.variable_scope(scope, 'ssd_300_vgg', [inputs], reuse=reuse):
# original vgg-16 blocks.
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
end_points['block1'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool1')
# block 2.
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
end_points['block2'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool2')
# block 3.
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
end_points['block3'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool3')
# block 4.
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
end_points['block4'] = net
net = slim.max_pool2d(net, [2, 2], scope='pool4')
# block 5.
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
end_points['block5'] = net
#注意處
net = slim.max_pool2d(net, [3, 3], stride=1, scope='pool5')
# additional ssd blocks.
# block 6: let's dilate the hell out of it!
#注意處
net = slim.conv2d(net, 1024, [3, 3], rate=6, scope='conv6')
end_points['block6'] = net
net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)
# block 7: 1x1 conv. because the ****.
#注意處
net = slim.conv2d(net, 1024, [1, 1], scope='conv7')
end_points['block7'] = net
net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)
# block 8/9/10/11: 1x1 and 3x3 convolutions stride 2 (except lasts).
end_point = 'block8'
with tf.variable_scope(end_point):
net = slim.conv2d(net, 256, [1, 1], scope='conv1x1')
#注意點：實際上相當於下面的卷積操作進行padding了
net = custom_layers.pad2d(net, pad=(1, 1))
net = slim.conv2d(net, 512, [3, 3], stride=2, scope='conv3x3', padding='valid')
end_points[end_point] = net
end_point = 'block9'
with tf.variable_scope(end_point):
net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
#注意點：實際上相當於下面的卷積操作進行padding了
net = custom_layers.pad2d(net, pad=(1, 1))
net = slim.conv2d(net, 256, [3, 3], stride=2, scope='conv3x3', padding='valid')
end_points[end_point] = net
end_point = 'block10'
with tf.variable_scope(end_point):
net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='valid')
end_points[end_point] = net
end_point = 'block11'
with tf.variable_scope(end_point):
net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='valid')
end_points[end_point] = net
經典網路結構梳理 SSD目標檢測演算法。
ssd全稱 single shot multibox detector 是2016年iccv的一篇 是目前為止主要的目標檢測演算法。演算法的主要優點 1.速度比faster rcnn快，精度比yolo高。在兼顧速度的同時，精度也很高。在不同的測試集下面結果都很不錯 2.為提高準確率在不同的特徵圖下面...
c s網路結構
採用c s 客戶端和服務端模式進行檔案和命令資料的傳輸，以及資料的交換。c s 和b s p2p等多種技術想結合。一，cs 網路的實現 採用傳統的客戶端和服務端模式，用tcp ip 協議進行資料的互動和傳遞。二，cs網路開發 模仿著名的灰鴿子軟體，灰鴿子的到來，給網路世界，帶來了新的活力，給人們帶來...
GPRS網路結構
gprs網路結構圖 bts base transceiver station，基站收發臺 bts可看作乙個無線數據機，負責移動訊號的接收和傳送處理 bsc base station controller，基站控制器 msc mobile switching center，移動交換中心 整個gprs網...