├── tests ├── dog.jpg └── daisy_test.jpg ├── npy2ckpt.py ├── test_GoogleNet.py ├── npy2ckpt_GoogleNet.py ├── npy2ckpt_OpenPose.py ├── README.md ├── network.py └── models └── imagenet-classes.txt /tests/dog.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alesolano/npy2ckpt/HEAD/tests/dog.jpg -------------------------------------------------------------------------------- /tests/daisy_test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alesolano/npy2ckpt/HEAD/tests/daisy_test.jpg -------------------------------------------------------------------------------- /npy2ckpt.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import tensorflow as tf 3 | import argparse 4 | 5 | # Modify this depending on the graph (.py) generated generated by caffe2tensorflow 6 | from models.net import Net 7 | 8 | def convert(model_data_path, output_path='/tmp/model.ckpt'): 9 | '''Convert the caffe model parameters to .ckpt.''' 10 | 11 | # Set the data specifications for the model 12 | width = None 13 | height = None 14 | channels = None 15 | input_node_name = None 16 | 17 | assert width and height and channels and input_node_name is not None, \ 18 | 'Please, fill the model parameters' 19 | 20 | # Create a placeholder for the input image 21 | input_node = tf.placeholder(tf.float32, 22 | shape=(None, height, width, channels), 23 | name='inputs') 24 | 25 | # Construct the network 26 | net = Net({'input_node_name': input_node}) 27 | 28 | with tf.Session() as sesh: 29 | # Load the converted parameters 30 | print('Loading the model...') 31 | net.load(model_data_path, sesh) 32 | 33 | saver = tf.train.Saver() 34 | save_path = saver.save(sesh, output_path) 35 | print("\nModel saved in file: %s" % save_path) 36 | 37 | 38 | def main(): 39 | # Parse arguments 40 | parser = argparse.ArgumentParser() 41 | parser.add_argument('model_path', help='Model parameters (.npy)') 42 | parser.add_argument('-output_path', help='Model checkpoints (.ckpt)') 43 | args = parser.parse_args() 44 | 45 | # Convert the model parameters to .ckpt 46 | if args.output_path: 47 | convert(args.model_path, output_path) 48 | else: 49 | convert(args.model_path) 50 | 51 | 52 | if __name__ == '__main__': 53 | main() 54 | -------------------------------------------------------------------------------- /test_GoogleNet.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import cv2 3 | import os 4 | 5 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # ignore debugging logs 6 | 7 | ### GRAPH ### 8 | 9 | tf.reset_default_graph() 10 | 11 | saver = tf.train.import_meta_graph('/tmp/googlenet_model.ckpt.meta') 12 | 13 | # Check the names of the tensors in your graph 14 | #for tensor in tf.get_default_graph().as_graph_def().node: 15 | # print(tensor.name) 16 | 17 | inputs = tf.get_default_graph().get_tensor_by_name('inputs:0') 18 | prob = tf.get_default_graph().get_tensor_by_name('prob:0') 19 | print('The input placeholder is expecting an array of shape {} and type {}'.format(inputs.shape, inputs.dtype)) 20 | 21 | 22 | ### CLASSES ### 23 | with open('./models/imagenet-classes.txt') as f: 24 | classes = f.read().splitlines() 25 | 26 | 27 | ### IMAGE PREPROCESSING ### 28 | img = cv2.imread('./tests/daisy_test.jpg') 29 | prep_img = cv2.resize(img, (224, 224), interpolation = cv2.INTER_CUBIC) 30 | prep_img = prep_img.reshape([1, 224, 224, 3]) 31 | print("The input image has been resized from {} to {}".format(img.shape, prep_img.shape)) 32 | 33 | assert list(prep_img.shape[1:]) == inputs.get_shape().as_list()[1:], \ 34 | 'Dimensions of the input image and the placeholder should match' 35 | print('Dimensions match!') 36 | 37 | 38 | ### SESSION ### 39 | with tf.Session() as sess: 40 | saver.restore(sess, '/tmp/googlenet_model.ckpt') 41 | 42 | prob_values = sess.run(prob, feed_dict={ 43 | inputs: prep_img 44 | }) 45 | 46 | 47 | ### RESULTS ### 48 | 49 | pred_idx = prob_values[0].argmax() 50 | pred_class = classes[pred_idx] 51 | pred_certain = round(100*prob_values[0][pred_idx], 2) # two decimals 52 | 53 | print("\nI'm {}% sure that this is a {}.".format(pred_certain, pred_class)) 54 | 55 | 56 | -------------------------------------------------------------------------------- /npy2ckpt_GoogleNet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import tensorflow as tf 3 | import argparse 4 | 5 | # Modify this depending on the graph (.py) generated generated by caffe2tensorflow 6 | from models.googlenet import GoogleNet 7 | 8 | def convert(model_data_path, output_path='/tmp/model.ckpt'): 9 | '''Convert the GoogleNet model parameters to .ckpt.''' 10 | 11 | # Set the data specifications for the GoogleNet model 12 | width = 224 13 | height = 224 14 | channels = 3 15 | input_node_name = 'data' 16 | 17 | assert width and height and channels and input_node_name is not None, \ 18 | 'Please, fill the model parameters' 19 | 20 | # Create a placeholder for the input image 21 | input_node = tf.placeholder(tf.float32, 22 | shape=(None, height, width, channels), 23 | name='inputs') 24 | 25 | # Construct the network 26 | net = GoogleNet({input_node_name: input_node}) 27 | 28 | with tf.Session() as sesh: 29 | # Load the converted parameters 30 | print('Loading the model...') 31 | net.load(model_data_path, sesh) 32 | 33 | saver = tf.train.Saver() 34 | save_path = saver.save(sesh, output_path) 35 | print("\nModel saved in file: %s" % save_path) 36 | 37 | 38 | def main(): 39 | # Parse arguments 40 | parser = argparse.ArgumentParser() 41 | parser.add_argument('model_path', help='Model parameters (.npy)') 42 | parser.add_argument('-output_path', help='Model checkpoints (.ckpt)') 43 | args = parser.parse_args() 44 | 45 | # Convert the model parameters to .ckpt 46 | if args.output_path: 47 | convert(args.model_path, output_path) 48 | else: 49 | convert(args.model_path) 50 | 51 | 52 | if __name__ == '__main__': 53 | main() 54 | -------------------------------------------------------------------------------- /npy2ckpt_OpenPose.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import tensorflow as tf 3 | import argparse 4 | 5 | # Modify this depending on the graph (.py) generated generated by caffe2tensorflow 6 | from models.openposenet import OpenPoseNet 7 | 8 | def convert(model_data_path, output_path='/tmp/model.ckpt'): 9 | '''Convert the OpenPose model parameters to .ckpt.''' 10 | 11 | # Set the data specifications for the OpenPose model 12 | width = 656 13 | height = 368 14 | channels = 3 15 | input_node_name = 'image' 16 | 17 | assert width and height and channels and input_node_name is not None, \ 18 | 'Please, fill the model parameters' 19 | 20 | # Create a placeholder for the input image 21 | input_node = tf.placeholder(tf.float32, 22 | shape=(None, height, width, channels), 23 | name='inputs') 24 | 25 | # Construct the network 26 | net = OpenPoseNet({input_node_name: input_node}) 27 | 28 | with tf.Session() as sesh: 29 | # Load the converted parameters 30 | print('Loading the model...') 31 | net.load(model_data_path, sesh) 32 | 33 | saver = tf.train.Saver() 34 | save_path = saver.save(sesh, output_path) 35 | print("\nModel saved in file: %s" % save_path) 36 | 37 | 38 | def main(): 39 | # Parse arguments 40 | parser = argparse.ArgumentParser() 41 | parser.add_argument('model_path', help='Model parameters (.npy)') 42 | parser.add_argument('-output_path', help='Model checkpoints (.ckpt)') 43 | args = parser.parse_args() 44 | 45 | # Convert the model parameters to .ckpt 46 | if args.output_path: 47 | convert(args.model_path, output_path) 48 | else: 49 | convert(args.model_path) 50 | 51 | 52 | if __name__ == '__main__': 53 | main() 54 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # npy2ckpt 2 | 3 | This is a repo to convert deep learning models from .npy format to .ckpt format. 4 | 5 | 6 | ### Set environment 7 | 8 | Using Anaconda: 9 | 10 | ``` 11 | conda create -n npy2ckpt python=3.6 12 | source activate npy2ckpt 13 | pip install tensorflow 14 | conda install -c menpo opencv3 15 | ``` 16 | 17 | ### Example of use (GoogleNet) 18 | 19 | Download GoogleNet model code (.py) and trained variables (.npy) from: http://www.deeplearningmodel.net/ 20 | (You can also find there the imagenet-classes.txt file) 21 | 22 | Move the files to the `models` folder. 23 | 24 | Change first line of the model code (.py): 25 | 26 | ``` 27 | #from kaffe.tensorflow import Network 28 | from network import Network 29 | ``` 30 | 31 | Run the converter code, pointing to the .npy file: 32 | 33 | `python npy2ckpt_GoogleNet.py models/googlenet.npy` 34 | 35 | **Take a look and how I set the parameters** (`width`, `height`, `channels`, `input_node_name`) in npy2ckpt_GoogleNet.py. 36 | 37 | Test the results: 38 | 39 | `python test_GoogleNet.py` 40 | 41 | 42 | ### Example of use (OpenPose) 43 | 44 | Following this [blog post](https://arvrjourney.com/human-pose-estimation-using-openpose-with-tensorflow-part-1-7dd4ca5c8027), use the covert.py function in the [Caffe2Tensorflow repo](https://github.com/ethereon/caffe-tensorflow) and move the output files to the `models` folder. 45 | 46 | Change first line of the model code (.py): 47 | 48 | ``` 49 | #from kaffe.tensorflow import Network 50 | from network import Network 51 | ``` 52 | 53 | Run the converter code, pointing to the .npy file: 54 | 55 | `python npy2ckpt_OpenPoseNet.py models/openposenet.npy` 56 | 57 | **Take a look and how I set the parameters** (`width`, `height`, `channels`, `input_node_name`) in npy2ckpt_OpenPoseNet.py. 58 | 59 | 60 | 61 | ### Why 62 | 63 | Amazing job here to convert models from Caffe to TensorFlow: https://github.com/ethereon/caffe-tensorflow 64 | . The thing is that the output is in .npy format, and I'm not very comfortable dealing with that. 65 | 66 | Most of the code is borrowed from that repo. I changed some things to update it to Python 3 and TensorFlow 1. 67 | -------------------------------------------------------------------------------- /network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import tensorflow as tf 3 | 4 | DEFAULT_PADDING = 'SAME' 5 | 6 | # Python 3: basestring is not a type 7 | basestring = (str,bytes) 8 | 9 | 10 | def layer(op): 11 | '''Decorator for composable network layers.''' 12 | 13 | def layer_decorated(self, *args, **kwargs): 14 | # Automatically set a name if not provided. 15 | name = kwargs.setdefault('name', self.get_unique_name(op.__name__)) 16 | # Figure out the layer inputs. 17 | if len(self.terminals) == 0: 18 | raise RuntimeError('No input variables found for layer %s.' % name) 19 | elif len(self.terminals) == 1: 20 | layer_input = self.terminals[0] 21 | else: 22 | layer_input = list(self.terminals) 23 | # Perform the operation and get the output. 24 | layer_output = op(self, layer_input, *args, **kwargs) 25 | # Add to layer LUT. 26 | self.layers[name] = layer_output 27 | # This output is now the input for the next layer. 28 | self.feed(layer_output) 29 | # Return self for chained calls. 30 | return self 31 | 32 | return layer_decorated 33 | 34 | 35 | class Network(object): 36 | 37 | def __init__(self, inputs, trainable=True): 38 | # The input nodes for this network 39 | self.inputs = inputs 40 | # The current list of terminal nodes 41 | self.terminals = [] 42 | # Mapping from layer names to layers 43 | self.layers = dict(inputs) 44 | # If true, the resulting variables are set as trainable 45 | self.trainable = trainable 46 | # Switch variable for dropout 47 | self.use_dropout = tf.placeholder_with_default(tf.constant(1.0), 48 | shape=[], 49 | name='use_dropout') 50 | self.setup() 51 | 52 | def setup(self): 53 | '''Construct the network. ''' 54 | raise NotImplementedError('Must be implemented by the subclass.') 55 | 56 | def load(self, data_path, session, ignore_missing=False): 57 | '''Load network weights. 58 | data_path: The path to the numpy-serialized network weights 59 | session: The current TensorFlow session 60 | ignore_missing: If true, serialized weights for missing layers are ignored. 61 | ''' 62 | data_dict = np.load(data_path, encoding='latin1').item() 63 | for op_name in data_dict: 64 | with tf.variable_scope(op_name, reuse=True): 65 | 66 | # Python3: dict has no module iteritems 67 | 68 | for param_name, data in data_dict[op_name].items(): 69 | try: 70 | var = tf.get_variable(param_name) 71 | session.run(var.assign(data)) 72 | except ValueError: 73 | if not ignore_missing: 74 | raise 75 | 76 | def feed(self, *args): 77 | '''Set the input(s) for the next operation by replacing the terminal nodes. 78 | The arguments can be either layer names or the actual layers. 79 | ''' 80 | assert len(args) != 0 81 | self.terminals = [] 82 | for fed_layer in args: 83 | if isinstance(fed_layer, basestring): 84 | try: 85 | fed_layer = self.layers[fed_layer] 86 | except KeyError: 87 | raise KeyError('Unknown layer name fed: %s' % fed_layer) 88 | self.terminals.append(fed_layer) 89 | return self 90 | 91 | def get_output(self): 92 | '''Returns the current network output.''' 93 | return self.terminals[-1] 94 | 95 | def get_unique_name(self, prefix): 96 | '''Returns an index-suffixed unique name for the given prefix. 97 | This is used for auto-generating layer names based on the type-prefix. 98 | ''' 99 | ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1 100 | return '%s_%d' % (prefix, ident) 101 | 102 | def make_var(self, name, shape): 103 | '''Creates a new TensorFlow variable.''' 104 | return tf.get_variable(name, shape, trainable=self.trainable) 105 | 106 | def validate_padding(self, padding): 107 | '''Verifies that the padding is one of the supported ones.''' 108 | assert padding in ('SAME', 'VALID') 109 | 110 | @layer 111 | def conv(self, 112 | input, 113 | k_h, 114 | k_w, 115 | c_o, 116 | s_h, 117 | s_w, 118 | name, 119 | relu=True, 120 | padding=DEFAULT_PADDING, 121 | group=1, 122 | biased=True): 123 | # Verify that the padding is acceptable 124 | self.validate_padding(padding) 125 | # Get the number of channels in the input 126 | c_i = input.get_shape()[-1] 127 | # Verify that the grouping parameter is valid 128 | assert c_i % group == 0 129 | assert c_o % group == 0 130 | 131 | # Convolution for a given input and kernel 132 | convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding) 133 | with tf.variable_scope(name) as scope: 134 | 135 | # TF 1.0: cannot use operand / with Dimension (c_i) and int (group) 136 | 137 | kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o]) 138 | if group == 1: 139 | # This is the common-case. Convolve the input without any further complications. 140 | output = convolve(input, kernel) 141 | else: 142 | # Split the input into groups and then convolve each of them independently 143 | input_groups = tf.split(3, group, input) 144 | kernel_groups = tf.split(3, group, kernel) 145 | output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)] 146 | # Concatenate the groups 147 | output = tf.concat(3, output_groups) 148 | # Add the biases 149 | if biased: 150 | biases = self.make_var('biases', [c_o]) 151 | output = tf.nn.bias_add(output, biases) 152 | if relu: 153 | # ReLU non-linearity 154 | output = tf.nn.relu(output, name=scope.name) 155 | return output 156 | 157 | @layer 158 | def relu(self, input, name): 159 | return tf.nn.relu(input, name=name) 160 | 161 | @layer 162 | def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING): 163 | self.validate_padding(padding) 164 | return tf.nn.max_pool(input, 165 | ksize=[1, k_h, k_w, 1], 166 | strides=[1, s_h, s_w, 1], 167 | padding=padding, 168 | name=name) 169 | 170 | @layer 171 | def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING): 172 | self.validate_padding(padding) 173 | return tf.nn.avg_pool(input, 174 | ksize=[1, k_h, k_w, 1], 175 | strides=[1, s_h, s_w, 1], 176 | padding=padding, 177 | name=name) 178 | 179 | @layer 180 | def lrn(self, input, radius, alpha, beta, name, bias=1.0): 181 | return tf.nn.local_response_normalization(input, 182 | depth_radius=radius, 183 | alpha=alpha, 184 | beta=beta, 185 | bias=bias, 186 | name=name) 187 | 188 | @layer 189 | def concat(self, inputs, axis, name): 190 | 191 | # TF 1.0: tf.concat has no argument concat_dim 192 | 193 | return tf.concat(axis=axis, values=inputs, name=name) 194 | 195 | @layer 196 | def add(self, inputs, name): 197 | return tf.add_n(inputs, name=name) 198 | 199 | @layer 200 | def fc(self, input, num_out, name, relu=True): 201 | with tf.variable_scope(name) as scope: 202 | input_shape = input.get_shape() 203 | if input_shape.ndims == 4: 204 | # The input is spatial. Vectorize it first. 205 | dim = 1 206 | for d in input_shape[1:].as_list(): 207 | dim *= d 208 | feed_in = tf.reshape(input, [-1, dim]) 209 | else: 210 | feed_in, dim = (input, input_shape[-1].value) 211 | weights = self.make_var('weights', shape=[dim, num_out]) 212 | biases = self.make_var('biases', [num_out]) 213 | op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b 214 | fc = op(feed_in, weights, biases, name=scope.name) 215 | return fc 216 | 217 | @layer 218 | def softmax(self, input, name): 219 | input_shape = map(lambda v: v.value, input.get_shape()) 220 | 221 | # Python3: map has no len() 222 | 223 | if len(list(input_shape)) > 2: 224 | # For certain models (like NiN), the singleton spatial dimensions 225 | # need to be explicitly squeezed, since they're not broadcast-able 226 | # in TensorFlow's NHWC ordering (unlike Caffe's NCHW). 227 | if input_shape[1] == 1 and input_shape[2] == 1: 228 | input = tf.squeeze(input, squeeze_dims=[1, 2]) 229 | else: 230 | raise ValueError('Rank 2 tensor input expected for softmax!') 231 | return tf.nn.softmax(input, name=name) 232 | 233 | @layer 234 | def batch_normalization(self, input, name, scale_offset=True, relu=False): 235 | # NOTE: Currently, only inference is supported 236 | with tf.variable_scope(name) as scope: 237 | shape = [input.get_shape()[-1]] 238 | if scale_offset: 239 | scale = self.make_var('scale', shape=shape) 240 | offset = self.make_var('offset', shape=shape) 241 | else: 242 | scale, offset = (None, None) 243 | output = tf.nn.batch_normalization( 244 | input, 245 | mean=self.make_var('mean', shape=shape), 246 | variance=self.make_var('variance', shape=shape), 247 | offset=offset, 248 | scale=scale, 249 | # TODO: This is the default Caffe batch norm eps 250 | # Get the actual eps from parameters 251 | variance_epsilon=1e-5, 252 | name=name) 253 | if relu: 254 | output = tf.nn.relu(output) 255 | return output 256 | 257 | @layer 258 | def dropout(self, input, keep_prob, name): 259 | keep = 1 - self.use_dropout + (self.use_dropout * keep_prob) 260 | return tf.nn.dropout(input, keep, name=name) 261 | -------------------------------------------------------------------------------- /models/imagenet-classes.txt: -------------------------------------------------------------------------------- 1 | tench, Tinca tinca 2 | goldfish, Carassius auratus 3 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias 4 | tiger shark, Galeocerdo cuvieri 5 | hammerhead, hammerhead shark 6 | electric ray, crampfish, numbfish, torpedo 7 | stingray 8 | cock 9 | hen 10 | ostrich, Struthio camelus 11 | brambling, Fringilla montifringilla 12 | goldfinch, Carduelis carduelis 13 | house finch, linnet, Carpodacus mexicanus 14 | junco, snowbird 15 | indigo bunting, indigo finch, indigo bird, Passerina cyanea 16 | robin, American robin, Turdus migratorius 17 | bulbul 18 | jay 19 | magpie 20 | chickadee 21 | water ouzel, dipper 22 | kite 23 | bald eagle, American eagle, Haliaeetus leucocephalus 24 | vulture 25 | great grey owl, great gray owl, Strix nebulosa 26 | European fire salamander, Salamandra salamandra 27 | common newt, Triturus vulgaris 28 | eft 29 | spotted salamander, Ambystoma maculatum 30 | axolotl, mud puppy, Ambystoma mexicanum 31 | bullfrog, Rana catesbeiana 32 | tree frog, tree-frog 33 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui 34 | loggerhead, loggerhead turtle, Caretta caretta 35 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea 36 | mud turtle 37 | terrapin 38 | box turtle, box tortoise 39 | banded gecko 40 | common iguana, iguana, Iguana iguana 41 | American chameleon, anole, Anolis carolinensis 42 | whiptail, whiptail lizard 43 | agama 44 | frilled lizard, Chlamydosaurus kingi 45 | alligator lizard 46 | Gila monster, Heloderma suspectum 47 | green lizard, Lacerta viridis 48 | African chameleon, Chamaeleo chamaeleon 49 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis 50 | African crocodile, Nile crocodile, Crocodylus niloticus 51 | American alligator, Alligator mississipiensis 52 | triceratops 53 | thunder snake, worm snake, Carphophis amoenus 54 | ringneck snake, ring-necked snake, ring snake 55 | hognose snake, puff adder, sand viper 56 | green snake, grass snake 57 | king snake, kingsnake 58 | garter snake, grass snake 59 | water snake 60 | vine snake 61 | night snake, Hypsiglena torquata 62 | boa constrictor, Constrictor constrictor 63 | rock python, rock snake, Python sebae 64 | Indian cobra, Naja naja 65 | green mamba 66 | sea snake 67 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus 68 | diamondback, diamondback rattlesnake, Crotalus adamanteus 69 | sidewinder, horned rattlesnake, Crotalus cerastes 70 | trilobite 71 | harvestman, daddy longlegs, Phalangium opilio 72 | scorpion 73 | black and gold garden spider, Argiope aurantia 74 | barn spider, Araneus cavaticus 75 | garden spider, Aranea diademata 76 | black widow, Latrodectus mactans 77 | tarantula 78 | wolf spider, hunting spider 79 | tick 80 | centipede 81 | black grouse 82 | ptarmigan 83 | ruffed grouse, partridge, Bonasa umbellus 84 | prairie chicken, prairie grouse, prairie fowl 85 | peacock 86 | quail 87 | partridge 88 | African grey, African gray, Psittacus erithacus 89 | macaw 90 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita 91 | lorikeet 92 | coucal 93 | bee eater 94 | hornbill 95 | hummingbird 96 | jacamar 97 | toucan 98 | drake 99 | red-breasted merganser, Mergus serrator 100 | goose 101 | black swan, Cygnus atratus 102 | tusker 103 | echidna, spiny anteater, anteater 104 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus 105 | wallaby, brush kangaroo 106 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus 107 | wombat 108 | jellyfish 109 | sea anemone, anemone 110 | brain coral 111 | flatworm, platyhelminth 112 | nematode, nematode worm, roundworm 113 | conch 114 | snail 115 | slug 116 | sea slug, nudibranch 117 | chiton, coat-of-mail shell, sea cradle, polyplacophore 118 | chambered nautilus, pearly nautilus, nautilus 119 | Dungeness crab, Cancer magister 120 | rock crab, Cancer irroratus 121 | fiddler crab 122 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica 123 | American lobster, Northern lobster, Maine lobster, Homarus americanus 124 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish 125 | crayfish, crawfish, crawdad, crawdaddy 126 | hermit crab 127 | isopod 128 | white stork, Ciconia ciconia 129 | black stork, Ciconia nigra 130 | spoonbill 131 | flamingo 132 | little blue heron, Egretta caerulea 133 | American egret, great white heron, Egretta albus 134 | bittern 135 | crane 136 | limpkin, Aramus pictus 137 | European gallinule, Porphyrio porphyrio 138 | American coot, marsh hen, mud hen, water hen, Fulica americana 139 | bustard 140 | ruddy turnstone, Arenaria interpres 141 | red-backed sandpiper, dunlin, Erolia alpina 142 | redshank, Tringa totanus 143 | dowitcher 144 | oystercatcher, oyster catcher 145 | pelican 146 | king penguin, Aptenodytes patagonica 147 | albatross, mollymawk 148 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus 149 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca 150 | dugong, Dugong dugon 151 | sea lion 152 | Chihuahua 153 | Japanese spaniel 154 | Maltese dog, Maltese terrier, Maltese 155 | Pekinese, Pekingese, Peke 156 | Shih-Tzu 157 | Blenheim spaniel 158 | papillon 159 | toy terrier 160 | Rhodesian ridgeback 161 | Afghan hound, Afghan 162 | basset, basset hound 163 | beagle 164 | bloodhound, sleuthhound 165 | bluetick 166 | black-and-tan coonhound 167 | Walker hound, Walker foxhound 168 | English foxhound 169 | redbone 170 | borzoi, Russian wolfhound 171 | Irish wolfhound 172 | Italian greyhound 173 | whippet 174 | Ibizan hound, Ibizan Podenco 175 | Norwegian elkhound, elkhound 176 | otterhound, otter hound 177 | Saluki, gazelle hound 178 | Scottish deerhound, deerhound 179 | Weimaraner 180 | Staffordshire bullterrier, Staffordshire bull terrier 181 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier 182 | Bedlington terrier 183 | Border terrier 184 | Kerry blue terrier 185 | Irish terrier 186 | Norfolk terrier 187 | Norwich terrier 188 | Yorkshire terrier 189 | wire-haired fox terrier 190 | Lakeland terrier 191 | Sealyham terrier, Sealyham 192 | Airedale, Airedale terrier 193 | cairn, cairn terrier 194 | Australian terrier 195 | Dandie Dinmont, Dandie Dinmont terrier 196 | Boston bull, Boston terrier 197 | miniature schnauzer 198 | giant schnauzer 199 | standard schnauzer 200 | Scotch terrier, Scottish terrier, Scottie 201 | Tibetan terrier, chrysanthemum dog 202 | silky terrier, Sydney silky 203 | soft-coated wheaten terrier 204 | West Highland white terrier 205 | Lhasa, Lhasa apso 206 | flat-coated retriever 207 | curly-coated retriever 208 | golden retriever 209 | Labrador retriever 210 | Chesapeake Bay retriever 211 | German short-haired pointer 212 | vizsla, Hungarian pointer 213 | English setter 214 | Irish setter, red setter 215 | Gordon setter 216 | Brittany spaniel 217 | clumber, clumber spaniel 218 | English springer, English springer spaniel 219 | Welsh springer spaniel 220 | cocker spaniel, English cocker spaniel, cocker 221 | Sussex spaniel 222 | Irish water spaniel 223 | kuvasz 224 | schipperke 225 | groenendael 226 | malinois 227 | briard 228 | kelpie 229 | komondor 230 | Old English sheepdog, bobtail 231 | Shetland sheepdog, Shetland sheep dog, Shetland 232 | collie 233 | Border collie 234 | Bouvier des Flandres, Bouviers des Flandres 235 | Rottweiler 236 | German shepherd, German shepherd dog, German police dog, alsatian 237 | Doberman, Doberman pinscher 238 | miniature pinscher 239 | Greater Swiss Mountain dog 240 | Bernese mountain dog 241 | Appenzeller 242 | EntleBucher 243 | boxer 244 | bull mastiff 245 | Tibetan mastiff 246 | French bulldog 247 | Great Dane 248 | Saint Bernard, St Bernard 249 | Eskimo dog, husky 250 | malamute, malemute, Alaskan malamute 251 | Siberian husky 252 | dalmatian, coach dog, carriage dog 253 | affenpinscher, monkey pinscher, monkey dog 254 | basenji 255 | pug, pug-dog 256 | Leonberg 257 | Newfoundland, Newfoundland dog 258 | Great Pyrenees 259 | Samoyed, Samoyede 260 | Pomeranian 261 | chow, chow chow 262 | keeshond 263 | Brabancon griffon 264 | Pembroke, Pembroke Welsh corgi 265 | Cardigan, Cardigan Welsh corgi 266 | toy poodle 267 | miniature poodle 268 | standard poodle 269 | Mexican hairless 270 | timber wolf, grey wolf, gray wolf, Canis lupus 271 | white wolf, Arctic wolf, Canis lupus tundrarum 272 | red wolf, maned wolf, Canis rufus, Canis niger 273 | coyote, prairie wolf, brush wolf, Canis latrans 274 | dingo, warrigal, warragal, Canis dingo 275 | dhole, Cuon alpinus 276 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus 277 | hyena, hyaena 278 | red fox, Vulpes vulpes 279 | kit fox, Vulpes macrotis 280 | Arctic fox, white fox, Alopex lagopus 281 | grey fox, gray fox, Urocyon cinereoargenteus 282 | tabby, tabby cat 283 | tiger cat 284 | Persian cat 285 | Siamese cat, Siamese 286 | Egyptian cat 287 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor 288 | lynx, catamount 289 | leopard, Panthera pardus 290 | snow leopard, ounce, Panthera uncia 291 | jaguar, panther, Panthera onca, Felis onca 292 | lion, king of beasts, Panthera leo 293 | tiger, Panthera tigris 294 | cheetah, chetah, Acinonyx jubatus 295 | brown bear, bruin, Ursus arctos 296 | American black bear, black bear, Ursus americanus, Euarctos americanus 297 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus 298 | sloth bear, Melursus ursinus, Ursus ursinus 299 | mongoose 300 | meerkat, mierkat 301 | tiger beetle 302 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 303 | ground beetle, carabid beetle 304 | long-horned beetle, longicorn, longicorn beetle 305 | leaf beetle, chrysomelid 306 | dung beetle 307 | rhinoceros beetle 308 | weevil 309 | fly 310 | bee 311 | ant, emmet, pismire 312 | grasshopper, hopper 313 | cricket 314 | walking stick, walkingstick, stick insect 315 | cockroach, roach 316 | mantis, mantid 317 | cicada, cicala 318 | leafhopper 319 | lacewing, lacewing fly 320 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk 321 | damselfly 322 | admiral 323 | ringlet, ringlet butterfly 324 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus 325 | cabbage butterfly 326 | sulphur butterfly, sulfur butterfly 327 | lycaenid, lycaenid butterfly 328 | starfish, sea star 329 | sea urchin 330 | sea cucumber, holothurian 331 | wood rabbit, cottontail, cottontail rabbit 332 | hare 333 | Angora, Angora rabbit 334 | hamster 335 | porcupine, hedgehog 336 | fox squirrel, eastern fox squirrel, Sciurus niger 337 | marmot 338 | beaver 339 | guinea pig, Cavia cobaya 340 | sorrel 341 | zebra 342 | hog, pig, grunter, squealer, Sus scrofa 343 | wild boar, boar, Sus scrofa 344 | warthog 345 | hippopotamus, hippo, river horse, Hippopotamus amphibius 346 | ox 347 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis 348 | bison 349 | ram, tup 350 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis 351 | ibex, Capra ibex 352 | hartebeest 353 | impala, Aepyceros melampus 354 | gazelle 355 | Arabian camel, dromedary, Camelus dromedarius 356 | llama 357 | weasel 358 | mink 359 | polecat, fitch, foulmart, foumart, Mustela putorius 360 | black-footed ferret, ferret, Mustela nigripes 361 | otter 362 | skunk, polecat, wood pussy 363 | badger 364 | armadillo 365 | three-toed sloth, ai, Bradypus tridactylus 366 | orangutan, orang, orangutang, Pongo pygmaeus 367 | gorilla, Gorilla gorilla 368 | chimpanzee, chimp, Pan troglodytes 369 | gibbon, Hylobates lar 370 | siamang, Hylobates syndactylus, Symphalangus syndactylus 371 | guenon, guenon monkey 372 | patas, hussar monkey, Erythrocebus patas 373 | baboon 374 | macaque 375 | langur 376 | colobus, colobus monkey 377 | proboscis monkey, Nasalis larvatus 378 | marmoset 379 | capuchin, ringtail, Cebus capucinus 380 | howler monkey, howler 381 | titi, titi monkey 382 | spider monkey, Ateles geoffroyi 383 | squirrel monkey, Saimiri sciureus 384 | Madagascar cat, ring-tailed lemur, Lemur catta 385 | indri, indris, Indri indri, Indri brevicaudatus 386 | Indian elephant, Elephas maximus 387 | African elephant, Loxodonta africana 388 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens 389 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 390 | barracouta, snoek 391 | eel 392 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch 393 | rock beauty, Holocanthus tricolor 394 | anemone fish 395 | sturgeon 396 | gar, garfish, garpike, billfish, Lepisosteus osseus 397 | lionfish 398 | puffer, pufferfish, blowfish, globefish 399 | abacus 400 | abaya 401 | academic gown, academic robe, judge's robe 402 | accordion, piano accordion, squeeze box 403 | acoustic guitar 404 | aircraft carrier, carrier, flattop, attack aircraft carrier 405 | airliner 406 | airship, dirigible 407 | altar 408 | ambulance 409 | amphibian, amphibious vehicle 410 | analog clock 411 | apiary, bee house 412 | apron 413 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin 414 | assault rifle, assault gun 415 | backpack, back pack, knapsack, packsack, rucksack, haversack 416 | bakery, bakeshop, bakehouse 417 | balance beam, beam 418 | balloon 419 | ballpoint, ballpoint pen, ballpen, Biro 420 | Band Aid 421 | banjo 422 | bannister, banister, balustrade, balusters, handrail 423 | barbell 424 | barber chair 425 | barbershop 426 | barn 427 | barometer 428 | barrel, cask 429 | barrow, garden cart, lawn cart, wheelbarrow 430 | baseball 431 | basketball 432 | bassinet 433 | bassoon 434 | bathing cap, swimming cap 435 | bath towel 436 | bathtub, bathing tub, bath, tub 437 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 438 | beacon, lighthouse, beacon light, pharos 439 | beaker 440 | bearskin, busby, shako 441 | beer bottle 442 | beer glass 443 | bell cote, bell cot 444 | bib 445 | bicycle-built-for-two, tandem bicycle, tandem 446 | bikini, two-piece 447 | binder, ring-binder 448 | binoculars, field glasses, opera glasses 449 | birdhouse 450 | boathouse 451 | bobsled, bobsleigh, bob 452 | bolo tie, bolo, bola tie, bola 453 | bonnet, poke bonnet 454 | bookcase 455 | bookshop, bookstore, bookstall 456 | bottlecap 457 | bow 458 | bow tie, bow-tie, bowtie 459 | brass, memorial tablet, plaque 460 | brassiere, bra, bandeau 461 | breakwater, groin, groyne, mole, bulwark, seawall, jetty 462 | breastplate, aegis, egis 463 | broom 464 | bucket, pail 465 | buckle 466 | bulletproof vest 467 | bullet train, bullet 468 | butcher shop, meat market 469 | cab, hack, taxi, taxicab 470 | caldron, cauldron 471 | candle, taper, wax light 472 | cannon 473 | canoe 474 | can opener, tin opener 475 | cardigan 476 | car mirror 477 | carousel, carrousel, merry-go-round, roundabout, whirligig 478 | carpenter's kit, tool kit 479 | carton 480 | car wheel 481 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM 482 | cassette 483 | cassette player 484 | castle 485 | catamaran 486 | CD player 487 | cello, violoncello 488 | cellular telephone, cellular phone, cellphone, cell, mobile phone 489 | chain 490 | chainlink fence 491 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour 492 | chain saw, chainsaw 493 | chest 494 | chiffonier, commode 495 | chime, bell, gong 496 | china cabinet, china closet 497 | Christmas stocking 498 | church, church building 499 | cinema, movie theater, movie theatre, movie house, picture palace 500 | cleaver, meat cleaver, chopper 501 | cliff dwelling 502 | cloak 503 | clog, geta, patten, sabot 504 | cocktail shaker 505 | coffee mug 506 | coffeepot 507 | coil, spiral, volute, whorl, helix 508 | combination lock 509 | computer keyboard, keypad 510 | confectionery, confectionary, candy store 511 | container ship, containership, container vessel 512 | convertible 513 | corkscrew, bottle screw 514 | cornet, horn, trumpet, trump 515 | cowboy boot 516 | cowboy hat, ten-gallon hat 517 | cradle 518 | crane 519 | crash helmet 520 | crate 521 | crib, cot 522 | Crock Pot 523 | croquet ball 524 | crutch 525 | cuirass 526 | dam, dike, dyke 527 | desk 528 | desktop computer 529 | dial telephone, dial phone 530 | diaper, nappy, napkin 531 | digital clock 532 | digital watch 533 | dining table, board 534 | dishrag, dishcloth 535 | dishwasher, dish washer, dishwashing machine 536 | disk brake, disc brake 537 | dock, dockage, docking facility 538 | dogsled, dog sled, dog sleigh 539 | dome 540 | doormat, welcome mat 541 | drilling platform, offshore rig 542 | drum, membranophone, tympan 543 | drumstick 544 | dumbbell 545 | Dutch oven 546 | electric fan, blower 547 | electric guitar 548 | electric locomotive 549 | entertainment center 550 | envelope 551 | espresso maker 552 | face powder 553 | feather boa, boa 554 | file, file cabinet, filing cabinet 555 | fireboat 556 | fire engine, fire truck 557 | fire screen, fireguard 558 | flagpole, flagstaff 559 | flute, transverse flute 560 | folding chair 561 | football helmet 562 | forklift 563 | fountain 564 | fountain pen 565 | four-poster 566 | freight car 567 | French horn, horn 568 | frying pan, frypan, skillet 569 | fur coat 570 | garbage truck, dustcart 571 | gasmask, respirator, gas helmet 572 | gas pump, gasoline pump, petrol pump, island dispenser 573 | goblet 574 | go-kart 575 | golf ball 576 | golfcart, golf cart 577 | gondola 578 | gong, tam-tam 579 | gown 580 | grand piano, grand 581 | greenhouse, nursery, glasshouse 582 | grille, radiator grille 583 | grocery store, grocery, food market, market 584 | guillotine 585 | hair slide 586 | hair spray 587 | half track 588 | hammer 589 | hamper 590 | hand blower, blow dryer, blow drier, hair dryer, hair drier 591 | hand-held computer, hand-held microcomputer 592 | handkerchief, hankie, hanky, hankey 593 | hard disc, hard disk, fixed disk 594 | harmonica, mouth organ, harp, mouth harp 595 | harp 596 | harvester, reaper 597 | hatchet 598 | holster 599 | home theater, home theatre 600 | honeycomb 601 | hook, claw 602 | hoopskirt, crinoline 603 | horizontal bar, high bar 604 | horse cart, horse-cart 605 | hourglass 606 | iPod 607 | iron, smoothing iron 608 | jack-o'-lantern 609 | jean, blue jean, denim 610 | jeep, landrover 611 | jersey, T-shirt, tee shirt 612 | jigsaw puzzle 613 | jinrikisha, ricksha, rickshaw 614 | joystick 615 | kimono 616 | knee pad 617 | knot 618 | lab coat, laboratory coat 619 | ladle 620 | lampshade, lamp shade 621 | laptop, laptop computer 622 | lawn mower, mower 623 | lens cap, lens cover 624 | letter opener, paper knife, paperknife 625 | library 626 | lifeboat 627 | lighter, light, igniter, ignitor 628 | limousine, limo 629 | liner, ocean liner 630 | lipstick, lip rouge 631 | Loafer 632 | lotion 633 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system 634 | loupe, jeweler's loupe 635 | lumbermill, sawmill 636 | magnetic compass 637 | mailbag, postbag 638 | mailbox, letter box 639 | maillot 640 | maillot, tank suit 641 | manhole cover 642 | maraca 643 | marimba, xylophone 644 | mask 645 | matchstick 646 | maypole 647 | maze, labyrinth 648 | measuring cup 649 | medicine chest, medicine cabinet 650 | megalith, megalithic structure 651 | microphone, mike 652 | microwave, microwave oven 653 | military uniform 654 | milk can 655 | minibus 656 | miniskirt, mini 657 | minivan 658 | missile 659 | mitten 660 | mixing bowl 661 | mobile home, manufactured home 662 | Model T 663 | modem 664 | monastery 665 | monitor 666 | moped 667 | mortar 668 | mortarboard 669 | mosque 670 | mosquito net 671 | motor scooter, scooter 672 | mountain bike, all-terrain bike, off-roader 673 | mountain tent 674 | mouse, computer mouse 675 | mousetrap 676 | moving van 677 | muzzle 678 | nail 679 | neck brace 680 | necklace 681 | nipple 682 | notebook, notebook computer 683 | obelisk 684 | oboe, hautboy, hautbois 685 | ocarina, sweet potato 686 | odometer, hodometer, mileometer, milometer 687 | oil filter 688 | organ, pipe organ 689 | oscilloscope, scope, cathode-ray oscilloscope, CRO 690 | overskirt 691 | oxcart 692 | oxygen mask 693 | packet 694 | paddle, boat paddle 695 | paddlewheel, paddle wheel 696 | padlock 697 | paintbrush 698 | pajama, pyjama, pj's, jammies 699 | palace 700 | panpipe, pandean pipe, syrinx 701 | paper towel 702 | parachute, chute 703 | parallel bars, bars 704 | park bench 705 | parking meter 706 | passenger car, coach, carriage 707 | patio, terrace 708 | pay-phone, pay-station 709 | pedestal, plinth, footstall 710 | pencil box, pencil case 711 | pencil sharpener 712 | perfume, essence 713 | Petri dish 714 | photocopier 715 | pick, plectrum, plectron 716 | pickelhaube 717 | picket fence, paling 718 | pickup, pickup truck 719 | pier 720 | piggy bank, penny bank 721 | pill bottle 722 | pillow 723 | ping-pong ball 724 | pinwheel 725 | pirate, pirate ship 726 | pitcher, ewer 727 | plane, carpenter's plane, woodworking plane 728 | planetarium 729 | plastic bag 730 | plate rack 731 | plow, plough 732 | plunger, plumber's helper 733 | Polaroid camera, Polaroid Land camera 734 | pole 735 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria 736 | poncho 737 | pool table, billiard table, snooker table 738 | pop bottle, soda bottle 739 | pot, flowerpot 740 | potter's wheel 741 | power drill 742 | prayer rug, prayer mat 743 | printer 744 | prison, prison house 745 | projectile, missile 746 | projector 747 | puck, hockey puck 748 | punching bag, punch bag, punching ball, punchball 749 | purse 750 | quill, quill pen 751 | quilt, comforter, comfort, puff 752 | racer, race car, racing car 753 | racket, racquet 754 | radiator 755 | radio, wireless 756 | radio telescope, radio reflector 757 | rain barrel 758 | recreational vehicle, RV, R.V. 759 | reel 760 | reflex camera 761 | refrigerator, icebox 762 | remote control, remote 763 | restaurant, eating house, eating place, eatery 764 | revolver, six-gun, six-shooter 765 | rifle 766 | rocking chair, rocker 767 | rotisserie 768 | rubber eraser, rubber, pencil eraser 769 | rugby ball 770 | rule, ruler 771 | running shoe 772 | safe 773 | safety pin 774 | saltshaker, salt shaker 775 | sandal 776 | sarong 777 | sax, saxophone 778 | scabbard 779 | scale, weighing machine 780 | school bus 781 | schooner 782 | scoreboard 783 | screen, CRT screen 784 | screw 785 | screwdriver 786 | seat belt, seatbelt 787 | sewing machine 788 | shield, buckler 789 | shoe shop, shoe-shop, shoe store 790 | shoji 791 | shopping basket 792 | shopping cart 793 | shovel 794 | shower cap 795 | shower curtain 796 | ski 797 | ski mask 798 | sleeping bag 799 | slide rule, slipstick 800 | sliding door 801 | slot, one-armed bandit 802 | snorkel 803 | snowmobile 804 | snowplow, snowplough 805 | soap dispenser 806 | soccer ball 807 | sock 808 | solar dish, solar collector, solar furnace 809 | sombrero 810 | soup bowl 811 | space bar 812 | space heater 813 | space shuttle 814 | spatula 815 | speedboat 816 | spider web, spider's web 817 | spindle 818 | sports car, sport car 819 | spotlight, spot 820 | stage 821 | steam locomotive 822 | steel arch bridge 823 | steel drum 824 | stethoscope 825 | stole 826 | stone wall 827 | stopwatch, stop watch 828 | stove 829 | strainer 830 | streetcar, tram, tramcar, trolley, trolley car 831 | stretcher 832 | studio couch, day bed 833 | stupa, tope 834 | submarine, pigboat, sub, U-boat 835 | suit, suit of clothes 836 | sundial 837 | sunglass 838 | sunglasses, dark glasses, shades 839 | sunscreen, sunblock, sun blocker 840 | suspension bridge 841 | swab, swob, mop 842 | sweatshirt 843 | swimming trunks, bathing trunks 844 | swing 845 | switch, electric switch, electrical switch 846 | syringe 847 | table lamp 848 | tank, army tank, armored combat vehicle, armoured combat vehicle 849 | tape player 850 | teapot 851 | teddy, teddy bear 852 | television, television system 853 | tennis ball 854 | thatch, thatched roof 855 | theater curtain, theatre curtain 856 | thimble 857 | thresher, thrasher, threshing machine 858 | throne 859 | tile roof 860 | toaster 861 | tobacco shop, tobacconist shop, tobacconist 862 | toilet seat 863 | torch 864 | totem pole 865 | tow truck, tow car, wrecker 866 | toyshop 867 | tractor 868 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi 869 | tray 870 | trench coat 871 | tricycle, trike, velocipede 872 | trimaran 873 | tripod 874 | triumphal arch 875 | trolleybus, trolley coach, trackless trolley 876 | trombone 877 | tub, vat 878 | turnstile 879 | typewriter keyboard 880 | umbrella 881 | unicycle, monocycle 882 | upright, upright piano 883 | vacuum, vacuum cleaner 884 | vase 885 | vault 886 | velvet 887 | vending machine 888 | vestment 889 | viaduct 890 | violin, fiddle 891 | volleyball 892 | waffle iron 893 | wall clock 894 | wallet, billfold, notecase, pocketbook 895 | wardrobe, closet, press 896 | warplane, military plane 897 | washbasin, handbasin, washbowl, lavabo, wash-hand basin 898 | washer, automatic washer, washing machine 899 | water bottle 900 | water jug 901 | water tower 902 | whiskey jug 903 | whistle 904 | wig 905 | window screen 906 | window shade 907 | Windsor tie 908 | wine bottle 909 | wing 910 | wok 911 | wooden spoon 912 | wool, woolen, woollen 913 | worm fence, snake fence, snake-rail fence, Virginia fence 914 | wreck 915 | yawl 916 | yurt 917 | web site, website, internet site, site 918 | comic book 919 | crossword puzzle, crossword 920 | street sign 921 | traffic light, traffic signal, stoplight 922 | book jacket, dust cover, dust jacket, dust wrapper 923 | menu 924 | plate 925 | guacamole 926 | consomme 927 | hot pot, hotpot 928 | trifle 929 | ice cream, icecream 930 | ice lolly, lolly, lollipop, popsicle 931 | French loaf 932 | bagel, beigel 933 | pretzel 934 | cheeseburger 935 | hotdog, hot dog, red hot 936 | mashed potato 937 | head cabbage 938 | broccoli 939 | cauliflower 940 | zucchini, courgette 941 | spaghetti squash 942 | acorn squash 943 | butternut squash 944 | cucumber, cuke 945 | artichoke, globe artichoke 946 | bell pepper 947 | cardoon 948 | mushroom 949 | Granny Smith 950 | strawberry 951 | orange 952 | lemon 953 | fig 954 | pineapple, ananas 955 | banana 956 | jackfruit, jak, jack 957 | custard apple 958 | pomegranate 959 | hay 960 | carbonara 961 | chocolate sauce, chocolate syrup 962 | dough 963 | meat loaf, meatloaf 964 | pizza, pizza pie 965 | potpie 966 | burrito 967 | red wine 968 | espresso 969 | cup 970 | eggnog 971 | alp 972 | bubble 973 | cliff, drop, drop-off 974 | coral reef 975 | geyser 976 | lakeside, lakeshore 977 | promontory, headland, head, foreland 978 | sandbar, sand bar 979 | seashore, coast, seacoast, sea-coast 980 | valley, vale 981 | volcano 982 | ballplayer, baseball player 983 | groom, bridegroom 984 | scuba diver 985 | rapeseed 986 | daisy 987 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum 988 | corn 989 | acorn 990 | hip, rose hip, rosehip 991 | buckeye, horse chestnut, conker 992 | coral fungus 993 | agaric 994 | gyromitra 995 | stinkhorn, carrion fungus 996 | earthstar 997 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa 998 | bolete 999 | ear, spike, capitulum 1000 | toilet tissue, toilet paper, bathroom tissue 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