├── AlexNet.py ├── Datagenerator.py ├── Finetune.py ├── README.md └── caffe_classes.py /AlexNet.py: -------------------------------------------------------------------------------- 1 | """ 2 | This is a implement of AlexNet with tensorflow and fork from Frederik Kratzert 3 | """ 4 | import tensorflow as tf 5 | import numpy as np 6 | 7 | class AlexNet(object): 8 | def __init__(self, input_x, keep_prob, num_classes, skip_layer, weights_path = 'Default'): 9 | # Initialization the parameters 10 | self.input_x = input_x 11 | self.keep_prob = keep_prob 12 | self.skip_layer = skip_layer 13 | if weights_path == 'Default' : 14 | self.weights_path = 'bvlc_alexnet.npy' 15 | else: 16 | self.weights_path = weights_path 17 | self.num_classes = num_classes 18 | # Create the AlexNet Network Define 19 | self.create() 20 | 21 | def create(self): 22 | #layer 1 23 | conv1 = self.conv(self.input_x,11,96,4,name = 'conv1', padding = 'VALID') 24 | pool1 = self.maxPooling(conv1, filter_size = 3, stride = 2, name = 'pool1', padding = 'VALID') 25 | norm1 = self.lrn(pool1,2,2e-05,0.75,name='norm1') 26 | #layer 2 27 | conv2 = self.conv(norm1,5,256,1,name = 'conv2',padding_num = 0, groups = 2) 28 | pool2 = self.maxPooling(conv2, filter_size = 3, stride = 2, name = 'pool2', padding = 'VALID') 29 | norm2 = self.lrn(pool2,2,2e-05,0.75,name='norm2') 30 | #layer 3 31 | conv3 = self.conv(norm2, 3, 384, 1, name = 'conv3') 32 | #layer 4 33 | conv4 = self.conv(conv3, 3, 384, 1, name = 'conv4',groups = 2) 34 | #layer 5 35 | conv5 = self.conv(conv4, 3, 256, 1, name = 'conv5', groups = 2) 36 | pool5 = self.maxPooling(conv5, filter_size = 3, stride = 2, name= 'pool5', padding = 'VALID') 37 | #layer 6 38 | flattened = tf.reshape(pool5, [-1,6*6*256]) 39 | fc6 = self.fc(input = flattened, num_in = 6*6*256, num_out = 4096, name = 'fc6', drop_ratio = 1.0-self.keep_prob, relu = True) 40 | #layer 7 41 | fc7 = self.fc(input = fc6, num_in = 4096, num_out = 4096, name = 'fc7', drop_ratio = 1.0 - self.keep_prob, relu = True) 42 | #layer 8 43 | self.fc8 = self.fc(input = fc7, num_in = 4096, num_out = self.num_classes, name = 'fc8', drop_ratio = 0, relu = False) 44 | 45 | #load pretrained weights 46 | def load_weights(self, session): 47 | weights_dict = np.load(self.weights_path, encoding = 'bytes').item() 48 | 49 | for op_name in weights_dict: 50 | if op_name not in self.skip_layer: 51 | with tf.variable_scope(op_name, reuse = True): 52 | for data in weights_dict[op_name]: 53 | if len(data.shape) == 1: 54 | var = tf.get_variable('biases',trainable=False) 55 | session.run(var.assign(data)) 56 | else: 57 | var = tf.get_variable('weights',trainable=False) 58 | session.run(var.assign(data)) 59 | 60 | def conv(self, x, kernel_height, num_kernels, stride, name, padding = 'SAME',padding_num = 0,groups = 1): 61 | print ('name is {} np.shape(input) {}'.format(name, np.shape(x))) 62 | 63 | input_channels = int(np.shape(x)[-1]) 64 | if not padding_num == 0: 65 | x = tf.pad(x,[[0,0],[padding_num,padding_num],[padding_num,padding_num],[0,0]]) 66 | convolve = lambda i,k:tf.nn.conv2d(i,k, strides = [1, stride, stride ,1], padding = padding) 67 | with tf.variable_scope(name) as scope: 68 | weights = tf.get_variable('weights', shape = [kernel_height, kernel_height, input_channels/groups, num_kernels]) 69 | biases = tf.get_variable('biases', shape = [num_kernels]) 70 | if groups == 1: 71 | conv = convolve(x,weights) 72 | else: 73 | input_groups = tf.split(axis=3,num_or_size_splits = groups, value = x) 74 | weights_groups = tf.split(axis = 3, num_or_size_splits = groups, value = weights) 75 | output_groups = [convolve(i,k) for i,k in zip(input_groups,weights_groups)] 76 | 77 | conv = tf.concat(axis = 3, values = output_groups) 78 | 79 | # add biases and avtive function 80 | withBias = tf.reshape(tf.nn.bias_add(conv,biases),conv.get_shape().as_list()) 81 | relu = tf.nn.relu(withBias) 82 | return relu 83 | 84 | def maxPooling(self, input,filter_size,stride,name,padding = 'SAME'): 85 | print ('name is {} np.shape(input) {}'.format(name,np.shape(input))) 86 | return tf.nn.max_pool(input,ksize=[1,filter_size,filter_size,1],strides = [1,stride,stride,1],padding = padding, name = name) 87 | 88 | def lrn(self, input,radius,alpha,beta,name,bias = 1.0): 89 | print ('name is {} np.shape(input) {}'.format(name,np.shape(input))) 90 | return tf.nn.local_response_normalization(input,depth_radius=radius, alpha=alpha,beta=beta,bias=bias,name=name) 91 | 92 | def fc(self, input,num_in,num_out,name,drop_ratio=0,relu = True): 93 | print ('name is {} np.shape(input) {}'.format(name,np.shape(input))) 94 | with tf.variable_scope(name) as scope: 95 | weights = tf.get_variable('weights',shape = [num_in,num_out],trainable=True) 96 | biases = tf.get_variable('biases',[num_out],trainable=True) 97 | # Linear 98 | act = tf.nn.xw_plus_b(input,weights,biases,name=scope.name) 99 | 100 | if relu == True: 101 | relu = tf.nn.relu(act) 102 | if drop_ratio == 0: 103 | return relu 104 | else: 105 | return tf.nn.dropout(relu,1.0-drop_ratio) 106 | else: 107 | if drop_ratio == 0: 108 | return act 109 | else: 110 | return tf.nn.dropout(act,1.0-drop_ratio) 111 | 112 | -------------------------------------------------------------------------------- /Datagenerator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | 4 | class ImageDataGenerator: 5 | def __init__(self, class_list, n_class, batch_size = 1, flip = True, shuffle = False, mean = np.array([104., 117., 124.]), scale_size = (227,227)): 6 | #initial params 7 | self.horizontal = flip 8 | self.batch_size = batch_size 9 | self.shuffle = shuffle 10 | self.class_list = class_list 11 | self.mean = mean 12 | self.scale_size = scale_size 13 | self.pointer = 0 14 | self.n_class = n_class 15 | self.read_class_list(class_list) 16 | if shuffle: 17 | self.shuffle_data() 18 | 19 | def read_class_list(self,class_list): 20 | with open(class_list) as f: 21 | lines = f.readlines() 22 | self.images = [] 23 | self.labels = [] 24 | for line in lines: 25 | items = line.split() 26 | self.images.append(items[0]) 27 | self.labels.append(items[1]) 28 | self.data_size = len(self.labels) 29 | 30 | def shuffle_data(self): 31 | images = self.images.copy() 32 | labels = self.labels.copy() 33 | self.images = [] 34 | self.labels = [] 35 | idx = np.random.permutation(self.data_size) 36 | for id in idx: 37 | self.images.append(images[id]) 38 | self.labels.append(labels[id]) 39 | 40 | def reset_pointer(self): 41 | self.pointer = 0 42 | if self.shuffle: 43 | self.shuffle_data() 44 | 45 | def getNext_batch(self): 46 | paths = self.images[self.pointer:self.pointer+self.batch_size] 47 | labels = self.labels[self.pointer:self.pointer+self.batch_size] 48 | self.pointer += self.batch_size 49 | 50 | images = np.ndarray([self.batch_size,self.scale_size[0],self.scale_size[1],3]) 51 | for i in range(len(paths)): 52 | image = cv2.imread(paths[i]) 53 | #print ('file name is {}'.format(paths[i])) 54 | #cv2.imshow(paths[i],image) 55 | #cv2.waitKey(0) 56 | if self.horizontal and np.random.random()<0.5: 57 | image = cv2.flip(image,1) 58 | image = cv2.resize(image,(self.scale_size[0],self.scale_size[1])) 59 | image = image.astype(np.float32) 60 | 61 | image -= self.mean 62 | images[i] = image 63 | 64 | one_hot_labels = np.zeros((self.batch_size,self.n_class)) 65 | for i in range(len(labels)): 66 | one_hot_labels[i][int(labels[i])] = 1 67 | return images,one_hot_labels -------------------------------------------------------------------------------- /Finetune.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import cv2 4 | import tensorflow as tf 5 | from datetime import datetime 6 | from AlexNet import AlexNet 7 | from Datagenerator import ImageDataGenerator 8 | 9 | def test_image(path_image,num_class,path_classes,weights_path = 'Default'): 10 | #x = tf.placeholder(tf.float32, [1,227,227,3]) 11 | x = cv2.imread(path_image) 12 | x = cv2.resize(x,(227,227)) 13 | x = x.astype(np.float32) 14 | 15 | x = np.reshape(x,[1,227,227,3]) 16 | y = tf.placeholder(tf.float32,[None,num_class]) 17 | model = AlexNet(x,0.5,1000,skip_layer = '', weights_path = weights_path) 18 | score = model.fc8 19 | max = tf.arg_max(score,1) 20 | with tf.Session() as sess: 21 | sess.run(tf.global_variables_initializer()) 22 | model.load_weights(sess) 23 | #score = model.fc8 24 | label_id = sess.run(max)[0] 25 | 26 | with open(path_classes) as f: 27 | lines = f.readlines() 28 | label = lines[label_id] 29 | print('image name is {} class_id is {} class_name is {}'.format(path_image,label_id,label)) 30 | cv2.imshow(label,cv2.imread(path_image)) 31 | cv2.waitKey(0) 32 | f.close() 33 | 34 | 35 | test_image('C:/Users/Rain/finetune_alexnet_with_tensorflow/images/zebra.jpeg',1000,'caffe_classes.py') 36 | 37 | def trainModels(): 38 | train_file = 'car-train.txt' 39 | val_file = 'car-test.txt' 40 | learning_rate = 0.01 41 | num_epochs = 10 42 | batch_size = 1 43 | dropout_rate = 0.5 44 | num_class = 15 45 | train_layers = ['fc8','fc7'] 46 | display_step = 1 47 | filewreiter_path = 'filewriter/cars' 48 | checkpoint_path = 'filewriter/' 49 | 50 | x = tf.placeholder(tf.float32,shape=[batch_size,227,227,3]) 51 | y = tf.placeholder(tf.float32,[None,num_class]) 52 | keep_prob = tf.placeholder(tf.float32) 53 | 54 | model = AlexNet(input_x = x,keep_prob = keep_prob,num_classes = num_class, skip_layer = train_layers,weights_path = 'Default') 55 | score = model.fc8 56 | var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers] 57 | with tf.name_scope('cross_ent'): 58 | loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=score,labels=y)) 59 | with tf.name_scope('train'): 60 | gradients = tf.gradients(ys = loss, xs = var_list) 61 | gradients = list(zip(gradients,var_list)) 62 | 63 | optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate) 64 | train_op = optimizer.apply_gradients(grads_and_vars = gradients) 65 | 66 | for gradient,var in gradients: 67 | tf.summary.histogram(var.name+'/gradient',gradient) 68 | for var in var_list: 69 | tf.summary.histogram(var.name,var) 70 | tf.summary.scalar('cross_entropy',loss) 71 | with tf.name_scope('accuracy'): 72 | correct_pred = tf.equal(tf.arg_max(score,1),tf.arg_max(y,1)) 73 | accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32)) 74 | 75 | tf.summary.scalar('accuracy',accuracy) 76 | merged_summary = tf.summary.merge_all() 77 | 78 | writer = tf.summary.FileWriter(filewreiter_path) 79 | saver = tf.train.Saver() 80 | train_generator = ImageDataGenerator(class_list = train_file, n_class = num_class, batch_size = batch_size, flip = True,shuffle=True) 81 | val_generator = ImageDataGenerator(class_list = val_file, n_class = num_class, batch_size=1, shuffle = False,flip = False) 82 | 83 | train_batchs_per_epochs = np.floor(train_generator.data_size/batch_size).astype(np.int16) 84 | val_batchs_per_epochs = np.floor(val_generator.data_size/batch_size).astype(np.int16) 85 | 86 | with tf.Session() as sess: 87 | sess.run(tf.global_variables_initializer()) 88 | writer.add_graph(sess.graph) 89 | model.load_weights(sess) 90 | 91 | print('{} start training ...'.format(datetime.now())) 92 | print('{} open TensorBoard at --logdir {}'.format(datetime.now(),filewreiter_path)) 93 | 94 | for epoch in range(num_epochs): 95 | print('{} Epochs number:{}'.format(datetime.now(),epoch+1)) 96 | step = 1 97 | while step < train_batchs_per_epochs: 98 | batch_xs, batch_ys = train_generator.getNext_batch() 99 | sess.run(train_op,feed_dict={x:batch_xs,y:batch_ys,keep_prob:dropout_rate}) 100 | if step%display_step == 0: 101 | s,get_loss = sess.run([merged_summary,loss],feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.5}) 102 | writer.add_summary(s,epoch*train_batchs_per_epochs+step) 103 | print('{} steps number:{} loss is {}'.format(datetime.now(),epoch*train_batchs_per_epochs+step,get_loss)) 104 | step += 1 105 | print('{} start validation'.format(datetime.now())) 106 | test_acc = 0. 107 | test_count = 0 108 | for _ in range(val_batchs_per_epochs): 109 | batch_xs, batch_ys = val_generator.getNext_batch() 110 | acc = sess.run(accuracy,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.}) 111 | test_acc += acc 112 | test_count +=1 113 | test_acc /= test_count 114 | print('{} validation Accuracy = {:.4f}'.format(datetime.now(),test_acc)) 115 | 116 | val_generator.reset_pointer() 117 | train_generator.reset_pointer() 118 | 119 | print('{} saving checkpoint of model ...'.format(datetime.now())) 120 | 121 | checkpoint_name = os.path.join(checkpoint_path,'model_epoch'+str(epoch+1)+'.ckpt') 122 | save_path = saver.save(sess,checkpoint_name) 123 | print('{} Model checkpoint saved at {}'.format(datetime.now(),checkpoint_name)) 124 | 125 | #trainModels() 126 | 127 | 128 | 129 | 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # tensorflow-AlexNet 2 | An implementment of AlexNet with TensorFlow 3 | 4 | AlexNet.py contains the defined of AlexNet 5 | 6 | Datagenerator.py contains the preprocess of image when training model with our dataset 7 | 8 | Finetune.py containst testImageNet images and training model with our dataset 9 | 10 | caffe_classes.py containst map label_id to label_name 11 | 12 | bvlc_alexnet.npy can be download at http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/ 13 | 14 | -------------------------------------------------------------------------------- /caffe_classes.py: -------------------------------------------------------------------------------- 1 | class_names = '''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'''.split("\n") --------------------------------------------------------------------------------