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vgg是在Very Deep Convolutional Networks for Large-Scale Image Recognition期刊上提出的。模型可以达到92.7%的测试准确度,在ImageNet的前5位。它的数据集包括1400万张图像，1000个类别。
vgg-16是一种深度卷积神经网络模型，16表示其深度，在图像分类等任务中取得了不错的效果。
vgg16 的宏观结构图如下。代码定义在tensorflow的vgg16.py文件 。注意，包括一个预处理层，使用RGB图像在0-255范围内的像素值减去平均值（在整个ImageNet图像训练集计算）。
模型权重 - vgg16_weights.npz
TensorFlow模型- vgg16.py
类名（输出模型到类名的映射） - imagenet_classes.py
示例图片输入 - laska.png
我们使用 特定的工具转换了原作者在 GitHub profile上公开可用的Caffe权重，并做了一些后续处理，以确保模型符合TensorFlow标准。最终实现可用的权重文件vgg16_weights.npz
下载所有的文件到同一文件夹下，然后运行 python vgg16.py
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import tensorflow as tfimport numpy as npfrom scipy.misc import imread,
imresizefrom imagenet_classes import class_namesclass vgg16:
def __init__(self, imgs, weights=None, sess=None): self.imgs = imgs
self.convlayers() self.fc_layers() self.probs = tf.nn.softmax(self.fc3l)
if weights is not None and sess is not None: self.load_weights(weights, sess)
def convlayers(self): self.parameters = [] # zero-mean input
with tf.name_scope('preprocess') as scope: mean = tf.constant([123.68, 116.779, 103.939],
dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean') images = self.imgs-mean
# conv1_1 with tf.name_scope('conv1_1') as scope: kernel = tf.Variable(tf
.truncated_normal([3, 3, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv1_2
with tf.name_scope('conv1_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv1_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# pool1 self.pool1 = tf.nn.max_pool(self.conv1_2,
ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool1') # conv2_1
with tf.name_scope('conv2_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv2_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv2_2 with tf.name_scope('conv2_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv2_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# pool2 self.pool2 = tf.nn.max_pool(self.conv2_2,
ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool2')
# conv3_1 with tf.name_scope('conv3_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv3_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv3_2 with tf.name_scope('conv3_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv3_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv3_3 with tf.name_scope('conv3_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases] # pool3 self.pool3 = tf.nn.max_pool(self.conv3_3,
ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool3') # conv4_1
with tf.name_scope('conv4_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512],
dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool3,
kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512],
dtype=tf.float32), trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases) self.conv4_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases] # conv4_2 with tf.name_scope('conv4_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv4_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv4_3 with tf.name_scope('conv4_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv4_3 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# pool4 self.pool4 = tf.nn.max_pool(self.conv4_3, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME',
name='pool4') # conv5_1 with tf.name_scope('conv5_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv5_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv5_2 with tf.name_scope('conv5_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv5_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# conv5_3 with tf.name_scope('conv5_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1],
padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases)
self.conv5_3 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases]
# pool5 self.pool5 = tf.nn.max_pool(self.conv5_3,
ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME', name='pool4') def fc_layers(self):
# fc1 with tf.name_scope('fc1') as scope:
shape = int(np.prod(self.pool5.get_shape()[1:]))
fc1w = tf.Variable(tf.truncated_normal([shape, 4096],
dtype=tf.float32, stddev=1e-1), name='weights')
fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=True, name='biases') pool5_flat = tf.reshape(self.pool5, [-1, shape])
fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b) self.fc1 = tf.nn.relu(fc1l)
self.parameters += [fc1w, fc1b] # fc2 with tf.name_scope('fc2') as scope:
fc2w = tf.Variable(tf.truncated_normal([4096, 4096],
dtype=tf.float32,
stddev=1e-1), name='weights')
fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=True, name='biases') fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)
self.fc2 = tf.nn.relu(fc2l) self.parameters += [fc2w, fc2b]
# fc3 with tf.name_scope('fc3') as scope:
fc3w = tf.Variable(tf.truncated_normal([4096, 1000],
dtype=tf.float32, stddev=1e-1), name='weights') fc3b = tf.Variable(tf.constant(1.0, shape=[1000],
dtype=tf.float32), trainable=True, name='biases')
self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)
self.parameters += [fc3w, fc3b] def load_weights(self, weight_file, sess):
weights = np.load(weight_file) keys = sorted(weights.keys())
for i, k in enumerate(keys): print i, k, np.shape(weights[k])
sess.run(self.parameters[i].assign(weights[k]))if __name__ == '__main__': sess = tf.Session()
imgs = tf.placeholder(tf.float32, [None, 224, 224, 3]) vgg = vgg16(imgs, 'vgg16_weights.npz', sess)
img1 = imread('laska.png', mode='RGB') img1 = imresize(img1, (224, 224))
prob = sess.run(vgg.probs, feed_dict={vgg.imgs: [img1]})[0] preds = (np.argsort(prob)[::-1])[0:5]
for p in preds: #print class_names[p], prob[p]
print("class_name {}: step {}".format(class_names[p], prob[p]))1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.
18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.
53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.
88.89.90.91.92.93.94.95.96.97.98.99.100.101.102.103.104.105.106.107.108.109.110.111.112.113.114.115.116.
117.118.119.120.121.122.123.124.125.126.127.128.129.130.131.132.133.134.135.136.137.138.139.140.141.142.
143.144.145.146.147.148.149.150.151.152.153.154.155.156.157.158.159.160.161.162.163.164.165.166.167.168.
169.170.171.172.173.174.175.176.177.178.179.180.181.182.183.184.185.186.187.188.189.190.191.192.193.194.195.
196.197.198.199.200.201.202.203.204.205.206.207.208.209.210.211.212.213.214.215.216.217.218.219.220.221.222.
223.224.225.226.227.228.229.230.231.232.233.234.235.236.237.238.239.240.241.242.243.244.245.246.247.248.249.
250.251.252.253.254.255.256.257.测试1：
输入图片为laska.png
运行结果：
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2018-03-23 11:04:38.311802: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions,
but these are available on your machine and could speed up CPU computations.
2018-03-23 11:04:38.311873: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions,
but these are available on your machine and could speed up CPU computations
.0 conv1_1_W (3, 3, 3, 64)1 conv1_1_b (64,)2 conv1_2_W (3, 3, 64, 64)3 conv1_2_b (64,)4
conv2_1_W (3, 3, 64, 128)5 conv2_1_b (128,)6 conv2_2_W (3, 3, 128, 128)7 conv2_2_b (128,)8
conv3_1_W (3, 3, 128, 256)9 conv3_1_b (256,)10 conv3_2_W (3, 3, 256, 256)11 conv3_2_b (256,)12
conv3_3_W (3, 3, 256, 256)13 conv3_3_b (256,)14 conv4_1_W (3, 3, 256, 512)15 conv4_1_b (512,)16
conv4_2_W (3, 3, 512, 512)17 conv4_2_b (512,)18 conv4_3_W (3, 3, 512, 512)19 conv4_3_b (512,)20
conv5_1_W (3, 3, 512, 512)21 conv5_1_b (512,)22 conv5_2_W (3, 3, 512, 512)23 conv5_2_b (512,)24
conv5_3_W (3, 3, 512, 512)25 conv5_3_b (512,)26 fc6_W (25088, 4096)27 fc6_b (4096,)28 fc7_W
(4096, 4096)29 fc7_b (4096,)30 fc8_W (4096, 1000)31 fc8_b (1000,)class_name **weasel**:
step 0.693385839462class_name polecat, fitch, foulmart, foumart, Mustela putorius:
step 0.175387635827class_name mink: step 0.12208583951class_name black-footed ferret,
ferret, Mustela nigripes: step 0.00887066219002class_name otter:
step 0.0001210832633661.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.
29.30.31.32.33.34.35.36.37.38.39.分类结果为weasel
测试2：
输入图片为多场景
运行结果为：
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2018-03-23 11:15:22.718228: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.2018-03-23 11:15:22.718297: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.0 conv1_1_W (3, 3, 3, 64)1 conv1_1_b (64,)2 conv1_2_W (3, 3, 64, 64)3 conv1_2_b (64,)4 conv2_1_W (3, 3, 64, 128)5 conv2_1_b (128,)6 conv2_2_W (3, 3, 128, 128)7 conv2_2_b (128,)8 conv3_1_W (3, 3, 128, 256)9 conv3_1_b (256,)10 conv3_2_W (3, 3, 256, 256)11 conv3_2_b (256,)12 conv3_3_W (3, 3, 256, 256)13 conv3_3_b (256,)14 conv4_1_W (3, 3, 256, 512)15 conv4_1_b (512,)16 conv4_2_W (3, 3, 512, 512)17 conv4_2_b (512,)18 conv4_3_W (3, 3, 512, 512)19 conv4_3_b (512,)20 conv5_1_W (3, 3, 512, 512)21 conv5_1_b (512,)22 conv5_2_W (3, 3, 512, 512)23 conv5_2_b (512,)24 conv5_3_W (3, 3, 512, 512)25 conv5_3_b (512,)26 fc6_W (25088, 4096)27 fc6_b (4096,)28 fc7_W (4096, 4096)29 fc7_b (4096,)30 fc8_W (4096, 1000)31 fc8_b (1000,)class_name alp: step 0.830908000469class_name church, church building: step 0.0817768126726class_name castle: step 0.024959910661class_name valley, vale: step 0.0158758834004class_name monastery: step 0.01006317697471.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.分类结果把高山，教堂，城堡，山谷，修道院都识别出来了，效果非常不错，虽然各种精度不高，但是类别是齐全的。
测试3：
输入图片为
运行结果为
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2018-03-23 11:34:50.490069: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX2 instructions, but these are available on your machine and
could speed up CPU computations.2018-03-23 11:34:50.490137: W tensorflow/core/platform/cpu_feature_guard
.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available
on your machine and could speed up CPU computations.0 conv1_1_W (3, 3, 3, 64)1 conv1_1_b (64,)2
conv1_2_W (3, 3, 64, 64)3 conv1_2_b (64,)4 conv2_1_W (3, 3, 64, 128)5 conv2_1_b (128,)6
conv2_2_W (3, 3, 128, 128)7 conv2_2_b (128,)8 conv3_1_W (3, 3, 128, 256)9 conv3_1_b (256,)10
conv3_2_W (3, 3, 256, 256)11 conv3_2_b (256,)12 conv3_3_W (3, 3, 256, 256)13 conv3_3_b (256,)14
conv4_1_W (3, 3, 256, 512)15 conv4_1_b (512,)16 conv4_2_W (3, 3, 512, 512)17 conv4_2_b (512,)18
conv4_3_W (3, 3, 512, 512)19 conv4_3_b (512,)20 conv5_1_W (3, 3, 512, 512)21 conv5_1_b (512,)22
conv5_2_W (3, 3, 512, 512)23 conv5_2_b (512,)24 conv5_3_W (3, 3, 512, 512)25 conv5_3_b (512,)26
fc6_W (25088, 4096)27 fc6_b (4096,)28 fc7_W (4096, 4096)29 fc7_b (4096,)30 fc8_W (4096, 1000)31
fc8_b (1000,)class_name cup: step 0.543631911278class_name coffee mug:
step 0.364796578884class_name pitcher, ewer: step 0.0259610358626class_name eggnog:
step 0.0117611540481class_name water jug: step 0.008063927292821.2.3.4.5.6.7.8.9.10.11.12.13.14.15
.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.分类结果为cup
测试4：
输入图片为
运行结果为
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2018-03-23 11:37:23.573090: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on
your machine and could speed up CPU computations.2018-03-23 11:37:23.573159:
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't
compiled to use FMA instructions, but these are available on your machine and could
speed up CPU computations.0 conv1_1_W (3, 3, 3, 64)1 conv1_1_b (64,)2 conv1_2_W (3, 3, 64, 64)3
conv1_2_b (64,)4 conv2_1_W (3, 3, 64, 128)5 conv2_1_b (128,)6 conv2_2_W (3, 3, 128, 128)7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)9 conv3_1_b (256,)10 conv3_2_W (3, 3, 256, 256)11 conv3_2_b (256,)12 conv3_3_W
(3, 3, 256, 256)13 conv3_3_b (256,)14 conv4_1_W (3, 3, 256, 512)15 conv4_1_b (512,)16 conv4_2_
W (3, 3, 512, 512)17 conv4_2_b (512,)18 conv4_3_W (3, 3, 512, 512)19 conv4_3_b (512,)20 conv5_1_W
(3, 3, 512, 512)21 conv5_1_b (512,)22 conv5_2_W (3, 3, 512, 512)23 conv5_2_b (512,)24 conv5_3_W
(3, 3, 512, 512)25 conv5_3_b (512,)26 fc6_W (25088, 4096)27 fc6_b (4096,)28 fc7_W (4096, 4096)29 fc7_b
(4096,)30 fc8_W (4096, 1000)31 fc8_b (1000,)class_name cellular telephone, cellular phone, cellphone,
cell, mobile phone: step 0.465327292681class_name iPod: step 0.10543012619class_name radio, wireless:
step 0.0810257941484class_name hard disc, hard disk, fixed disk: step 0.0789099931717class_name
modem: step 0.06031630560761.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.
27.28.29.30.31.32.33.34.35.36.37.38.39.分类结果为 cellular telephone
测试5：
输入图片为
运行结果为
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2018-03-23 11:40:40.956946: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX2 instructions, but these are available on your machine and could
speed up CPU computations.2018-03-23 11:40:40.957016: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine
and could speed up CPU computations.0 conv1_1_W (3, 3, 3, 64)1 conv1_1_b (64,)2 conv1_2_W (3, 3, 64, 64)3
conv1_2_b (64,)4 conv2_1_W (3, 3, 64, 128)5 conv2_1_b (128,)6 conv2_2_W (3, 3, 128, 128)7 conv2_2_b (128,)8
conv3_1_W (3, 3, 128, 256)9 conv3_1_b (256,)10 conv3_2_W (3, 3, 256, 256)11 conv3_2_b (256,)12 conv3_3_
W (3, 3, 256, 256)13 conv3_3_b (256,)14 conv4_1_W (3, 3, 256, 512)15 conv4_1_b (512,)16 conv4_2_W (3, 3,
512, 512)17 conv4_2_b (512,)18 conv4_3_W (3, 3, 512, 512)19 conv4_3_b (512,)20 conv5_1_W (3, 3, 512, 512)21
conv5_1_b (512,)22 conv5_2_W (3, 3, 512, 512)23 conv5_2_b (512,)24 conv5_3_W (3, 3, 512, 512)25
conv5_3_b (512,)26 fc6_W (25088, 4096)27 fc6_b (4096,)28 fc7_W (4096, 4096)29 fc7_b (4096,)
30 fc8_W (4096, 1000)31 fc8_b (1000,)class_name water bottle: step 0.75726544857class_name pop bottle,
soda bottle: step 0.0976340323687class_name nipple: step 0.0622750669718class_name water
jug: step 0.0233819428831class_name soap dispenser: step 0.01793665438891.2.3.4.5.6.7.8.9.10.
11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.分类结果为 water bottle
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