Repository: GKalliatakis/Keras-VGG16-places365
Branch: master
Commit: 959389fa9356
Files: 12
Total size: 101.6 KB
Directory structure:
gitextract_h64dlm66/
├── .gitignore
├── .idea/
│ └── vcs.xml
├── CONTRIBUTING.md
├── ISSUE_TEMPLATE.md
├── LICENSE
├── README.md
├── categories_hybrid1365.txt
├── categories_places365.txt
├── places365_class_index.json
├── places_utils.py
├── vgg16_hybrid_places_1365.py
└── vgg16_places_365.py
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FILE CONTENTS
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FILE: .gitignore
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FILE: .idea/vcs.xml
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FILE: CONTRIBUTING.md
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# On Github Issues and Pull Requests
Found a bug? Have a new feature to suggest? Want to contribute changes to the codebase? Make sure to read this first.
## Bug reporting
Your code doesn't work, and you have determined that the issue lies with Keras-VGG16-places365? Follow these steps to report a bug.
1. Your bug may already be fixed. Make sure to update to the current Keras master branch (``` pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps ```), as well as the latest Theano/TensorFlow/CNTK master branch.
To easily update Theano: `pip install git+git://github.com/Theano/Theano.git --upgrade`
2. Search for similar issues. It's possible somebody has encountered this bug already. Also remember to check out Keras' [FAQ](http://keras.io/faq/). Still having a problem? Open an issue on Github to let us know.
3. Make sure you provide us with useful information about your configuration: what OS are you using? What Keras backend are you using? Are you running on GPU?
4. Provide us with a script to reproduce the issue. This script should be runnable as-is and should not require external data download (use randomly generated data if you need to run a model on some test data). We recommend that you use Github Gists to post your code.
5. If possible, take a stab at fixing the bug yourself --if you can!
The more information you provide, the easier it is for us to validate that there is a bug and the faster we'll be able to take action. If you want your issue to be resolved quickly, following the steps above is crucial.
---
## Requesting a Feature
You can also use Github issues to request features you would like to see in Keras-VGG16-places365.
1. Provide a clear and detailed explanation of the feature you want and why it's important to add.
2. Provide code snippets demonstrating the API you have in mind and illustrating the use cases of your feature. Of course, you don't need to write any real code at this point!
3. After discussing the feature you may choose to attempt a Pull Request. If you're at all able, start writing some code. We always have more work to do than time to do it. If you can write some code then that will speed the process along.
---
## Pull Requests
**Where should I submit my pull request?**
1. **Keras-VGG16-places365 improvements and bugfixes** go to the [Keras-VGG16-places365 `master` branch](https://github.com/GKalliatakis/Keras-VGG16-places365/tree/master).
Here's a quick guide to submitting your improvements:
1. Write the code (or get others to write it). This is the hard part!
2. When committing, use appropriate, descriptive commit messages.
3. Update the documentation. If introducing new functionality, make sure you include code snippets demonstrating the usage of your new feature.
4. Submit your PR. If your changes have been approved in a previous discussion, your PR is likely to be merged promptly.
---
## Adding new examples
Even if you don't contribute to the Keras-VGG16-places365 source code, if you have an application of Keras-VGG16-places365 that is concise and powerful, please consider adding it to our collection of examples.
================================================
FILE: ISSUE_TEMPLATE.md
================================================
Please make sure that the boxes below are checked before you submit your issue.
Thank you!
- [ ] Check that you are up-to-date with the master branch of Keras. You can update with:
```
pip install git+git://github.com/keras-team/keras.git --upgrade --no-deps
```
- [ ] If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found [here](https://www.tensorflow.org/get_started/os_setup).
- [ ] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
```
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
```
- [ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2017 Grigorios Kalliatakis
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Keras | VGG16 Places365 - VGG16 CNN models pre-trained on Places365-Standard for scene classification
[](https://github.com/GKalliatakis/Keras-VGG16-places365/blob/master/LICENSE)
[](https://twitter.com/intent/tweet?text=Keras%20code%20and%20weights%20files%20for%20the%20VGG16-places365%20and%20VGG16-hybrid1365%20CNNs%20for%20scene%20classification&url=https://github.com/GKalliatakis/Keras-VGG16-places365&hashtags=ML,DeepLearning,CNNs,SceneRecognition,Keras,Places365)
## You have just found the Keras models of the pre-trained VGG16 CNNs on [Places365-Standard](http://places2.csail.mit.edu/download.html) (~1.8 million images from 365 scene categories).
### Overview
CNNs trained on Places365 database (latest subset of [Places2 Database](http://places2.csail.mit.edu)) could be directly used for scene recognition, while the deep scene features from the higher level layer of CNN could be used as generic features for visual recognition.
### Paper
The Keras models has been obtained by directly converting the [Caffe models](https://github.com/CSAILVision/places365) provived by the authors (all the original Caffe-based resources can be found there).
More details about the architecture of the networks can be found in the following paper:
Places: A 10 million Image Database for Scene Recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Please consider citing the above paper if you use the pre-trained CNN models.
### Contents:
This repository contains code for the following Keras models:
- VGG16-places365
- VGG16-hybrid1365
### Usage:
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth".
Pre-trained weights can be automatically loaded upon instantiation (`weights='places'` argument in model constructor for all image models). Weights are automatically downloaded.
## Examples
### Classify Places classes with VGG16-places365
```python
import os
import urllib2
import numpy as np
from PIL import Image
from cv2 import resize
from vgg16_places_365 import VGG16_Places365
TEST_IMAGE_URL = 'http://places2.csail.mit.edu/imgs/demo/6.jpg'
image = Image.open(urllib2.urlopen(TEST_IMAGE_URL))
image = np.array(image, dtype=np.uint8)
image = resize(image, (224, 224))
image = np.expand_dims(image, 0)
model = VGG16_Places365(weights='places')
predictions_to_return = 5
preds = model.predict(image)[0]
top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
# load the class label
file_name = 'categories_places365.txt'
if not os.access(file_name, os.W_OK):
synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_places365.txt'
os.system('wget ' + synset_url)
classes = list()
with open(file_name) as class_file:
for line in class_file:
classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
print('--SCENE CATEGORIES:')
# output the prediction
for i in range(0, 5):
print(classes[top_preds[i]])
# --PREDICTED SCENE CATEGORIES:
# cafeteria
# food_court
# restaurant_patio
# banquet_hall
# restaurant
```
### Extract features from images with VGG16-hybrid1365
```python
import urllib2
import numpy as np
from PIL import Image
from cv2 import resize
from vgg16_hybrid_places_1365 import VGG16_Hybrid_1365
TEST_IMAGE_URL = 'http://places2.csail.mit.edu/imgs/demo/6.jpg'
image = Image.open(urllib2.urlopen(TEST_IMAGE_URL))
image = np.array(image, dtype=np.uint8)
image = resize(image, (224, 224))
image = np.expand_dims(image, 0)
model = VGG16_Hybrid_1365(weights='places', include_top=False)
features = model.predict(image)
```
### References
- [A 10 million Image Database for Scene Recognition](http://places2.csail.mit.edu/PAMI_places.pdf) - please cite this paper if you use the VGG16-places365 or VGG16-hybrid1365 model in your work.
- [Learning Deep Features for Scene Recognition using Places Database](https://arxiv.org/abs/1512.03385)
Additionally, don't forget to cite this repo if you use these models:
@misc{gkallia2017keras_places365,
title={Keras-VGG16-Places365},
author={Grigorios Kalliatakis},
year={2017},
publisher={GitHub},
howpublished={\url{https://github.com/GKalliatakis/Keras-VGG16-places365}},
}
### Licensing
- All code in this repository is under the MIT license as specified by the [LICENSE file](https://github.com/GKalliatakis/Keras-VGG16-places365/blob/master/LICENSE).
- The VGG16-places365 and VGG16-hybrid1365 weights were originally ported from the ones [released by CSAILVision](https://github.com/CSAILVision/places365) under the [MIT license](https://github.com/CSAILVision/places365/blob/master/LICENSE) but resulted in an [identical & low confidence predictions issue](https://github.com/GKalliatakis/Keras-VGG16-places365/issues/5).
- The current version of the VGG16-places365 and VGG16-hybrid1365 weights are the ones released by [landmark-recognition-challenge](https://github.com/antorsae/landmark-recognition-challenge) under the [GNU General Public License v3.0](https://github.com/antorsae/landmark-recognition-challenge/blob/master/LICENSE)
We are always interested in how these models are being used, so if you found them useful or plan to make a release of code based on or using this package, it would be great to hear from you.
### Where to get other trained models?
More info on downloading, converting, and submitting other models can be found on the main [Keras | Application Zoo repository](https://github.com/GKalliatakis/Keras-Application-Zoo).
### Questions and Comments
If you have any suggestions or bugs to report you can pull a request or start a discussion.
______________________________________________________
[dill]:
[git-repo-url]:
[john gruber]:
[df1]:
[markdown-it]:
[Ace Editor]:
[node.js]:
[Twitter Bootstrap]:
[jQuery]:
[@tjholowaychuk]:
[express]:
[AngularJS]:
[Gulp]:
[PlDb]:
[PlGh]:
[PlGd]:
[PlOd]:
[PlMe]:
[PlGa]:
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FILE: categories_hybrid1365.txt
================================================
n01440764 tench, Tinca tinca 0
n01443537 goldfish, Carassius auratus 1
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias 2
n01491361 tiger shark, Galeocerdo cuvieri 3
n01494475 hammerhead, hammerhead shark 4
n01496331 electric ray, crampfish, numbfish, torpedo 5
n01498041 stingray 6
n01514668 cock 7
n01514859 hen 8
n01518878 ostrich, Struthio camelus 9
n01530575 brambling, Fringilla montifringilla 10
n01531178 goldfinch, Carduelis carduelis 11
n01532829 house finch, linnet, Carpodacus mexicanus 12
n01534433 junco, snowbird 13
n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea 14
n01558993 robin, American robin, Turdus migratorius 15
n01560419 bulbul 16
n01580077 jay 17
n01582220 magpie 18
n01592084 chickadee 19
n01601694 water ouzel, dipper 20
n01608432 kite 21
n01614925 bald eagle, American eagle, Haliaeetus leucocephalus 22
n01616318 vulture 23
n01622779 great grey owl, great gray owl, Strix nebulosa 24
n01629819 European fire salamander, Salamandra salamandra 25
n01630670 common newt, Triturus vulgaris 26
n01631663 eft 27
n01632458 spotted salamander, Ambystoma maculatum 28
n01632777 axolotl, mud puppy, Ambystoma mexicanum 29
n01641577 bullfrog, Rana catesbeiana 30
n01644373 tree frog, tree-frog 31
n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui 32
n01664065 loggerhead, loggerhead turtle, Caretta caretta 33
n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea 34
n01667114 mud turtle 35
n01667778 terrapin 36
n01669191 box turtle, box tortoise 37
n01675722 banded gecko 38
n01677366 common iguana, iguana, Iguana iguana 39
n01682714 American chameleon, anole, Anolis carolinensis 40
n01685808 whiptail, whiptail lizard 41
n01687978 agama 42
n01688243 frilled lizard, Chlamydosaurus kingi 43
n01689811 alligator lizard 44
n01692333 Gila monster, Heloderma suspectum 45
n01693334 green lizard, Lacerta viridis 46
n01694178 African chameleon, Chamaeleo chamaeleon 47
n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis 48
n01697457 African crocodile, Nile crocodile, Crocodylus niloticus 49
n01698640 American alligator, Alligator mississipiensis 50
n01704323 triceratops 51
n01728572 thunder snake, worm snake, Carphophis amoenus 52
n01728920 ringneck snake, ring-necked snake, ring snake 53
n01729322 hognose snake, puff adder, sand viper 54
n01729977 green snake, grass snake 55
n01734418 king snake, kingsnake 56
n01735189 garter snake, grass snake 57
n01737021 water snake 58
n01739381 vine snake 59
n01740131 night snake, Hypsiglena torquata 60
n01742172 boa constrictor, Constrictor constrictor 61
n01744401 rock python, rock snake, Python sebae 62
n01748264 Indian cobra, Naja naja 63
n01749939 green mamba 64
n01751748 sea snake 65
n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus 66
n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus 67
n01756291 sidewinder, horned rattlesnake, Crotalus cerastes 68
n01768244 trilobite 69
n01770081 harvestman, daddy longlegs, Phalangium opilio 70
n01770393 scorpion 71
n01773157 black and gold garden spider, Argiope aurantia 72
n01773549 barn spider, Araneus cavaticus 73
n01773797 garden spider, Aranea diademata 74
n01774384 black widow, Latrodectus mactans 75
n01774750 tarantula 76
n01775062 wolf spider, hunting spider 77
n01776313 tick 78
n01784675 centipede 79
n01795545 black grouse 80
n01796340 ptarmigan 81
n01797886 ruffed grouse, partridge, Bonasa umbellus 82
n01798484 prairie chicken, prairie grouse, prairie fowl 83
n01806143 peacock 84
n01806567 quail 85
n01807496 partridge 86
n01817953 African grey, African gray, Psittacus erithacus 87
n01818515 macaw 88
n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita 89
n01820546 lorikeet 90
n01824575 coucal 91
n01828970 bee eater 92
n01829413 hornbill 93
n01833805 hummingbird 94
n01843065 jacamar 95
n01843383 toucan 96
n01847000 drake 97
n01855032 red-breasted merganser, Mergus serrator 98
n01855672 goose 99
n01860187 black swan, Cygnus atratus 100
n01871265 tusker 101
n01872401 echidna, spiny anteater, anteater 102
n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus 103
n01877812 wallaby, brush kangaroo 104
n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus 105
n01883070 wombat 106
n01910747 jellyfish 107
n01914609 sea anemone, anemone 108
n01917289 brain coral 109
n01924916 flatworm, platyhelminth 110
n01930112 nematode, nematode worm, roundworm 111
n01943899 conch 112
n01944390 snail 113
n01945685 slug 114
n01950731 sea slug, nudibranch 115
n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore 116
n01968897 chambered nautilus, pearly nautilus, nautilus 117
n01978287 Dungeness crab, Cancer magister 118
n01978455 rock crab, Cancer irroratus 119
n01980166 fiddler crab 120
n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica 121
n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus 122
n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish 123
n01985128 crayfish, crawfish, crawdad, crawdaddy 124
n01986214 hermit crab 125
n01990800 isopod 126
n02002556 white stork, Ciconia ciconia 127
n02002724 black stork, Ciconia nigra 128
n02006656 spoonbill 129
n02007558 flamingo 130
n02009229 little blue heron, Egretta caerulea 131
n02009912 American egret, great white heron, Egretta albus 132
n02011460 bittern 133
n02012849 crane 134
n02013706 limpkin, Aramus pictus 135
n02017213 European gallinule, Porphyrio porphyrio 136
n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana 137
n02018795 bustard 138
n02025239 ruddy turnstone, Arenaria interpres 139
n02027492 red-backed sandpiper, dunlin, Erolia alpina 140
n02028035 redshank, Tringa totanus 141
n02033041 dowitcher 142
n02037110 oystercatcher, oyster catcher 143
n02051845 pelican 144
n02056570 king penguin, Aptenodytes patagonica 145
n02058221 albatross, mollymawk 146
n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus 147
n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca 148
n02074367 dugong, Dugong dugon 149
n02077923 sea lion 150
n02085620 Chihuahua 151
n02085782 Japanese spaniel 152
n02085936 Maltese dog, Maltese terrier, Maltese 153
n02086079 Pekinese, Pekingese, Peke 154
n02086240 Shih-Tzu 155
n02086646 Blenheim spaniel 156
n02086910 papillon 157
n02087046 toy terrier 158
n02087394 Rhodesian ridgeback 159
n02088094 Afghan hound, Afghan 160
n02088238 basset, basset hound 161
n02088364 beagle 162
n02088466 bloodhound, sleuthhound 163
n02088632 bluetick 164
n02089078 black-and-tan coonhound 165
n02089867 Walker hound, Walker foxhound 166
n02089973 English foxhound 167
n02090379 redbone 168
n02090622 borzoi, Russian wolfhound 169
n02090721 Irish wolfhound 170
n02091032 Italian greyhound 171
n02091134 whippet 172
n02091244 Ibizan hound, Ibizan Podenco 173
n02091467 Norwegian elkhound, elkhound 174
n02091635 otterhound, otter hound 175
n02091831 Saluki, gazelle hound 176
n02092002 Scottish deerhound, deerhound 177
n02092339 Weimaraner 178
n02093256 Staffordshire bullterrier, Staffordshire bull terrier 179
n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier 180
n02093647 Bedlington terrier 181
n02093754 Border terrier 182
n02093859 Kerry blue terrier 183
n02093991 Irish terrier 184
n02094114 Norfolk terrier 185
n02094258 Norwich terrier 186
n02094433 Yorkshire terrier 187
n02095314 wire-haired fox terrier 188
n02095570 Lakeland terrier 189
n02095889 Sealyham terrier, Sealyham 190
n02096051 Airedale, Airedale terrier 191
n02096177 cairn, cairn terrier 192
n02096294 Australian terrier 193
n02096437 Dandie Dinmont, Dandie Dinmont terrier 194
n02096585 Boston bull, Boston terrier 195
n02097047 miniature schnauzer 196
n02097130 giant schnauzer 197
n02097209 standard schnauzer 198
n02097298 Scotch terrier, Scottish terrier, Scottie 199
n02097474 Tibetan terrier, chrysanthemum dog 200
n02097658 silky terrier, Sydney silky 201
n02098105 soft-coated wheaten terrier 202
n02098286 West Highland white terrier 203
n02098413 Lhasa, Lhasa apso 204
n02099267 flat-coated retriever 205
n02099429 curly-coated retriever 206
n02099601 golden retriever 207
n02099712 Labrador retriever 208
n02099849 Chesapeake Bay retriever 209
n02100236 German short-haired pointer 210
n02100583 vizsla, Hungarian pointer 211
n02100735 English setter 212
n02100877 Irish setter, red setter 213
n02101006 Gordon setter 214
n02101388 Brittany spaniel 215
n02101556 clumber, clumber spaniel 216
n02102040 English springer, English springer spaniel 217
n02102177 Welsh springer spaniel 218
n02102318 cocker spaniel, English cocker spaniel, cocker 219
n02102480 Sussex spaniel 220
n02102973 Irish water spaniel 221
n02104029 kuvasz 222
n02104365 schipperke 223
n02105056 groenendael 224
n02105162 malinois 225
n02105251 briard 226
n02105412 kelpie 227
n02105505 komondor 228
n02105641 Old English sheepdog, bobtail 229
n02105855 Shetland sheepdog, Shetland sheep dog, Shetland 230
n02106030 collie 231
n02106166 Border collie 232
n02106382 Bouvier des Flandres, Bouviers des Flandres 233
n02106550 Rottweiler 234
n02106662 German shepherd, German shepherd dog, German police dog, alsatian 235
n02107142 Doberman, Doberman pinscher 236
n02107312 miniature pinscher 237
n02107574 Greater Swiss Mountain dog 238
n02107683 Bernese mountain dog 239
n02107908 Appenzeller 240
n02108000 EntleBucher 241
n02108089 boxer 242
n02108422 bull mastiff 243
n02108551 Tibetan mastiff 244
n02108915 French bulldog 245
n02109047 Great Dane 246
n02109525 Saint Bernard, St Bernard 247
n02109961 Eskimo dog, husky 248
n02110063 malamute, malemute, Alaskan malamute 249
n02110185 Siberian husky 250
n02110341 dalmatian, coach dog, carriage dog 251
n02110627 affenpinscher, monkey pinscher, monkey dog 252
n02110806 basenji 253
n02110958 pug, pug-dog 254
n02111129 Leonberg 255
n02111277 Newfoundland, Newfoundland dog 256
n02111500 Great Pyrenees 257
n02111889 Samoyed, Samoyede 258
n02112018 Pomeranian 259
n02112137 chow, chow chow 260
n02112350 keeshond 261
n02112706 Brabancon griffon 262
n02113023 Pembroke, Pembroke Welsh corgi 263
n02113186 Cardigan, Cardigan Welsh corgi 264
n02113624 toy poodle 265
n02113712 miniature poodle 266
n02113799 standard poodle 267
n02113978 Mexican hairless 268
n02114367 timber wolf, grey wolf, gray wolf, Canis lupus 269
n02114548 white wolf, Arctic wolf, Canis lupus tundrarum 270
n02114712 red wolf, maned wolf, Canis rufus, Canis niger 271
n02114855 coyote, prairie wolf, brush wolf, Canis latrans 272
n02115641 dingo, warrigal, warragal, Canis dingo 273
n02115913 dhole, Cuon alpinus 274
n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus 275
n02117135 hyena, hyaena 276
n02119022 red fox, Vulpes vulpes 277
n02119789 kit fox, Vulpes macrotis 278
n02120079 Arctic fox, white fox, Alopex lagopus 279
n02120505 grey fox, gray fox, Urocyon cinereoargenteus 280
n02123045 tabby, tabby cat 281
n02123159 tiger cat 282
n02123394 Persian cat 283
n02123597 Siamese cat, Siamese 284
n02124075 Egyptian cat 285
n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor 286
n02127052 lynx, catamount 287
n02128385 leopard, Panthera pardus 288
n02128757 snow leopard, ounce, Panthera uncia 289
n02128925 jaguar, panther, Panthera onca, Felis onca 290
n02129165 lion, king of beasts, Panthera leo 291
n02129604 tiger, Panthera tigris 292
n02130308 cheetah, chetah, Acinonyx jubatus 293
n02132136 brown bear, bruin, Ursus arctos 294
n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus 295
n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus 296
n02134418 sloth bear, Melursus ursinus, Ursus ursinus 297
n02137549 mongoose 298
n02138441 meerkat, mierkat 299
n02165105 tiger beetle 300
n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle 301
n02167151 ground beetle, carabid beetle 302
n02168699 long-horned beetle, longicorn, longicorn beetle 303
n02169497 leaf beetle, chrysomelid 304
n02172182 dung beetle 305
n02174001 rhinoceros beetle 306
n02177972 weevil 307
n02190166 fly 308
n02206856 bee 309
n02219486 ant, emmet, pismire 310
n02226429 grasshopper, hopper 311
n02229544 cricket 312
n02231487 walking stick, walkingstick, stick insect 313
n02233338 cockroach, roach 314
n02236044 mantis, mantid 315
n02256656 cicada, cicala 316
n02259212 leafhopper 317
n02264363 lacewing, lacewing fly 318
n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk 319
n02268853 damselfly 320
n02276258 admiral 321
n02277742 ringlet, ringlet butterfly 322
n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus 323
n02280649 cabbage butterfly 324
n02281406 sulphur butterfly, sulfur butterfly 325
n02281787 lycaenid, lycaenid butterfly 326
n02317335 starfish, sea star 327
n02319095 sea urchin 328
n02321529 sea cucumber, holothurian 329
n02325366 wood rabbit, cottontail, cottontail rabbit 330
n02326432 hare 331
n02328150 Angora, Angora rabbit 332
n02342885 hamster 333
n02346627 porcupine, hedgehog 334
n02356798 fox squirrel, eastern fox squirrel, Sciurus niger 335
n02361337 marmot 336
n02363005 beaver 337
n02364673 guinea pig, Cavia cobaya 338
n02389026 sorrel 339
n02391049 zebra 340
n02395406 hog, pig, grunter, squealer, Sus scrofa 341
n02396427 wild boar, boar, Sus scrofa 342
n02397096 warthog 343
n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius 344
n02403003 ox 345
n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis 346
n02410509 bison 347
n02412080 ram, tup 348
n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis 349
n02417914 ibex, Capra ibex 350
n02422106 hartebeest 351
n02422699 impala, Aepyceros melampus 352
n02423022 gazelle 353
n02437312 Arabian camel, dromedary, Camelus dromedarius 354
n02437616 llama 355
n02441942 weasel 356
n02442845 mink 357
n02443114 polecat, fitch, foulmart, foumart, Mustela putorius 358
n02443484 black-footed ferret, ferret, Mustela nigripes 359
n02444819 otter 360
n02445715 skunk, polecat, wood pussy 361
n02447366 badger 362
n02454379 armadillo 363
n02457408 three-toed sloth, ai, Bradypus tridactylus 364
n02480495 orangutan, orang, orangutang, Pongo pygmaeus 365
n02480855 gorilla, Gorilla gorilla 366
n02481823 chimpanzee, chimp, Pan troglodytes 367
n02483362 gibbon, Hylobates lar 368
n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus 369
n02484975 guenon, guenon monkey 370
n02486261 patas, hussar monkey, Erythrocebus patas 371
n02486410 baboon 372
n02487347 macaque 373
n02488291 langur 374
n02488702 colobus, colobus monkey 375
n02489166 proboscis monkey, Nasalis larvatus 376
n02490219 marmoset 377
n02492035 capuchin, ringtail, Cebus capucinus 378
n02492660 howler monkey, howler 379
n02493509 titi, titi monkey 380
n02493793 spider monkey, Ateles geoffroyi 381
n02494079 squirrel monkey, Saimiri sciureus 382
n02497673 Madagascar cat, ring-tailed lemur, Lemur catta 383
n02500267 indri, indris, Indri indri, Indri brevicaudatus 384
n02504013 Indian elephant, Elephas maximus 385
n02504458 African elephant, Loxodonta africana 386
n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens 387
n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca 388
n02514041 barracouta, snoek 389
n02526121 eel 390
n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch 391
n02606052 rock beauty, Holocanthus tricolor 392
n02607072 anemone fish 393
n02640242 sturgeon 394
n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus 395
n02643566 lionfish 396
n02655020 puffer, pufferfish, blowfish, globefish 397
n02666196 abacus 398
n02667093 abaya 399
n02669723 academic gown, academic robe, judge's robe 400
n02672831 accordion, piano accordion, squeeze box 401
n02676566 acoustic guitar 402
n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier 403
n02690373 airliner 404
n02692877 airship, dirigible 405
n02699494 altar 406
n02701002 ambulance 407
n02704792 amphibian, amphibious vehicle 408
n02708093 analog clock 409
n02727426 apiary, bee house 410
n02730930 apron 411
n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin 412
n02749479 assault rifle, assault gun 413
n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack 414
n02776631 bakery, bakeshop, bakehouse 415
n02777292 balance beam, beam 416
n02782093 balloon 417
n02783161 ballpoint, ballpoint pen, ballpen, Biro 418
n02786058 Band Aid 419
n02787622 banjo 420
n02788148 bannister, banister, balustrade, balusters, handrail 421
n02790996 barbell 422
n02791124 barber chair 423
n02791270 barbershop 424
n02793495 barn 425
n02794156 barometer 426
n02795169 barrel, cask 427
n02797295 barrow, garden cart, lawn cart, wheelbarrow 428
n02799071 baseball 429
n02802426 basketball 430
n02804414 bassinet 431
n02804610 bassoon 432
n02807133 bathing cap, swimming cap 433
n02808304 bath towel 434
n02808440 bathtub, bathing tub, bath, tub 435
n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 436
n02814860 beacon, lighthouse, beacon light, pharos 437
n02815834 beaker 438
n02817516 bearskin, busby, shako 439
n02823428 beer bottle 440
n02823750 beer glass 441
n02825657 bell cote, bell cot 442
n02834397 bib 443
n02835271 bicycle-built-for-two, tandem bicycle, tandem 444
n02837789 bikini, two-piece 445
n02840245 binder, ring-binder 446
n02841315 binoculars, field glasses, opera glasses 447
n02843684 birdhouse 448
n02859443 boathouse 449
n02860847 bobsled, bobsleigh, bob 450
n02865351 bolo tie, bolo, bola tie, bola 451
n02869837 bonnet, poke bonnet 452
n02870880 bookcase 453
n02871525 bookshop, bookstore, bookstall 454
n02877765 bottlecap 455
n02879718 bow 456
n02883205 bow tie, bow-tie, bowtie 457
n02892201 brass, memorial tablet, plaque 458
n02892767 brassiere, bra, bandeau 459
n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty 460
n02895154 breastplate, aegis, egis 461
n02906734 broom 462
n02909870 bucket, pail 463
n02910353 buckle 464
n02916936 bulletproof vest 465
n02917067 bullet train, bullet 466
n02927161 butcher shop, meat market 467
n02930766 cab, hack, taxi, taxicab 468
n02939185 caldron, cauldron 469
n02948072 candle, taper, wax light 470
n02950826 cannon 471
n02951358 canoe 472
n02951585 can opener, tin opener 473
n02963159 cardigan 474
n02965783 car mirror 475
n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig 476
n02966687 carpenter's kit, tool kit 477
n02971356 carton 478
n02974003 car wheel 479
n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM 480
n02978881 cassette 481
n02979186 cassette player 482
n02980441 castle 483
n02981792 catamaran 484
n02988304 CD player 485
n02992211 cello, violoncello 486
n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone 487
n02999410 chain 488
n03000134 chainlink fence 489
n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour 490
n03000684 chain saw, chainsaw 491
n03014705 chest 492
n03016953 chiffonier, commode 493
n03017168 chime, bell, gong 494
n03018349 china cabinet, china closet 495
n03026506 Christmas stocking 496
n03028079 church, church building 497
n03032252 cinema, movie theater, movie theatre, movie house, picture palace 498
n03041632 cleaver, meat cleaver, chopper 499
n03042490 cliff dwelling 500
n03045698 cloak 501
n03047690 clog, geta, patten, sabot 502
n03062245 cocktail shaker 503
n03063599 coffee mug 504
n03063689 coffeepot 505
n03065424 coil, spiral, volute, whorl, helix 506
n03075370 combination lock 507
n03085013 computer keyboard, keypad 508
n03089624 confectionery, confectionary, candy store 509
n03095699 container ship, containership, container vessel 510
n03100240 convertible 511
n03109150 corkscrew, bottle screw 512
n03110669 cornet, horn, trumpet, trump 513
n03124043 cowboy boot 514
n03124170 cowboy hat, ten-gallon hat 515
n03125729 cradle 516
n03126707 crane 517
n03127747 crash helmet 518
n03127925 crate 519
n03131574 crib, cot 520
n03133878 Crock Pot 521
n03134739 croquet ball 522
n03141823 crutch 523
n03146219 cuirass 524
n03160309 dam, dike, dyke 525
n03179701 desk 526
n03180011 desktop computer 527
n03187595 dial telephone, dial phone 528
n03188531 diaper, nappy, napkin 529
n03196217 digital clock 530
n03197337 digital watch 531
n03201208 dining table, board 532
n03207743 dishrag, dishcloth 533
n03207941 dishwasher, dish washer, dishwashing machine 534
n03208938 disk brake, disc brake 535
n03216828 dock, dockage, docking facility 536
n03218198 dogsled, dog sled, dog sleigh 537
n03220513 dome 538
n03223299 doormat, welcome mat 539
n03240683 drilling platform, offshore rig 540
n03249569 drum, membranophone, tympan 541
n03250847 drumstick 542
n03255030 dumbbell 543
n03259280 Dutch oven 544
n03271574 electric fan, blower 545
n03272010 electric guitar 546
n03272562 electric locomotive 547
n03290653 entertainment center 548
n03291819 envelope 549
n03297495 espresso maker 550
n03314780 face powder 551
n03325584 feather boa, boa 552
n03337140 file, file cabinet, filing cabinet 553
n03344393 fireboat 554
n03345487 fire engine, fire truck 555
n03347037 fire screen, fireguard 556
n03355925 flagpole, flagstaff 557
n03372029 flute, transverse flute 558
n03376595 folding chair 559
n03379051 football helmet 560
n03384352 forklift 561
n03388043 fountain 562
n03388183 fountain pen 563
n03388549 four-poster 564
n03393912 freight car 565
n03394916 French horn, horn 566
n03400231 frying pan, frypan, skillet 567
n03404251 fur coat 568
n03417042 garbage truck, dustcart 569
n03424325 gasmask, respirator, gas helmet 570
n03425413 gas pump, gasoline pump, petrol pump, island dispenser 571
n03443371 goblet 572
n03444034 go-kart 573
n03445777 golf ball 574
n03445924 golfcart, golf cart 575
n03447447 gondola 576
n03447721 gong, tam-tam 577
n03450230 gown 578
n03452741 grand piano, grand 579
n03457902 greenhouse, nursery, glasshouse 580
n03459775 grille, radiator grille 581
n03461385 grocery store, grocery, food market, market 582
n03467068 guillotine 583
n03476684 hair slide 584
n03476991 hair spray 585
n03478589 half track 586
n03481172 hammer 587
n03482405 hamper 588
n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier 589
n03485407 hand-held computer, hand-held microcomputer 590
n03485794 handkerchief, hankie, hanky, hankey 591
n03492542 hard disc, hard disk, fixed disk 592
n03494278 harmonica, mouth organ, harp, mouth harp 593
n03495258 harp 594
n03496892 harvester, reaper 595
n03498962 hatchet 596
n03527444 holster 597
n03529860 home theater, home theatre 598
n03530642 honeycomb 599
n03532672 hook, claw 600
n03534580 hoopskirt, crinoline 601
n03535780 horizontal bar, high bar 602
n03538406 horse cart, horse-cart 603
n03544143 hourglass 604
n03584254 iPod 605
n03584829 iron, smoothing iron 606
n03590841 jack-o'-lantern 607
n03594734 jean, blue jean, denim 608
n03594945 jeep, landrover 609
n03595614 jersey, T-shirt, tee shirt 610
n03598930 jigsaw puzzle 611
n03599486 jinrikisha, ricksha, rickshaw 612
n03602883 joystick 613
n03617480 kimono 614
n03623198 knee pad 615
n03627232 knot 616
n03630383 lab coat, laboratory coat 617
n03633091 ladle 618
n03637318 lampshade, lamp shade 619
n03642806 laptop, laptop computer 620
n03649909 lawn mower, mower 621
n03657121 lens cap, lens cover 622
n03658185 letter opener, paper knife, paperknife 623
n03661043 library 624
n03662601 lifeboat 625
n03666591 lighter, light, igniter, ignitor 626
n03670208 limousine, limo 627
n03673027 liner, ocean liner 628
n03676483 lipstick, lip rouge 629
n03680355 Loafer 630
n03690938 lotion 631
n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system 632
n03692522 loupe, jeweler's loupe 633
n03697007 lumbermill, sawmill 634
n03706229 magnetic compass 635
n03709823 mailbag, postbag 636
n03710193 mailbox, letter box 637
n03710637 maillot 638
n03710721 maillot, tank suit 639
n03717622 manhole cover 640
n03720891 maraca 641
n03721384 marimba, xylophone 642
n03724870 mask 643
n03729826 matchstick 644
n03733131 maypole 645
n03733281 maze, labyrinth 646
n03733805 measuring cup 647
n03742115 medicine chest, medicine cabinet 648
n03743016 megalith, megalithic structure 649
n03759954 microphone, mike 650
n03761084 microwave, microwave oven 651
n03763968 military uniform 652
n03764736 milk can 653
n03769881 minibus 654
n03770439 miniskirt, mini 655
n03770679 minivan 656
n03773504 missile 657
n03775071 mitten 658
n03775546 mixing bowl 659
n03776460 mobile home, manufactured home 660
n03777568 Model T 661
n03777754 modem 662
n03781244 monastery 663
n03782006 monitor 664
n03785016 moped 665
n03786901 mortar 666
n03787032 mortarboard 667
n03788195 mosque 668
n03788365 mosquito net 669
n03791053 motor scooter, scooter 670
n03792782 mountain bike, all-terrain bike, off-roader 671
n03792972 mountain tent 672
n03793489 mouse, computer mouse 673
n03794056 mousetrap 674
n03796401 moving van 675
n03803284 muzzle 676
n03804744 nail 677
n03814639 neck brace 678
n03814906 necklace 679
n03825788 nipple 680
n03832673 notebook, notebook computer 681
n03837869 obelisk 682
n03838899 oboe, hautboy, hautbois 683
n03840681 ocarina, sweet potato 684
n03841143 odometer, hodometer, mileometer, milometer 685
n03843555 oil filter 686
n03854065 organ, pipe organ 687
n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO 688
n03866082 overskirt 689
n03868242 oxcart 690
n03868863 oxygen mask 691
n03871628 packet 692
n03873416 paddle, boat paddle 693
n03874293 paddlewheel, paddle wheel 694
n03874599 padlock 695
n03876231 paintbrush 696
n03877472 pajama, pyjama, pj's, jammies 697
n03877845 palace 698
n03884397 panpipe, pandean pipe, syrinx 699
n03887697 paper towel 700
n03888257 parachute, chute 701
n03888605 parallel bars, bars 702
n03891251 park bench 703
n03891332 parking meter 704
n03895866 passenger car, coach, carriage 705
n03899768 patio, terrace 706
n03902125 pay-phone, pay-station 707
n03903868 pedestal, plinth, footstall 708
n03908618 pencil box, pencil case 709
n03908714 pencil sharpener 710
n03916031 perfume, essence 711
n03920288 Petri dish 712
n03924679 photocopier 713
n03929660 pick, plectrum, plectron 714
n03929855 pickelhaube 715
n03930313 picket fence, paling 716
n03930630 pickup, pickup truck 717
n03933933 pier 718
n03935335 piggy bank, penny bank 719
n03937543 pill bottle 720
n03938244 pillow 721
n03942813 ping-pong ball 722
n03944341 pinwheel 723
n03947888 pirate, pirate ship 724
n03950228 pitcher, ewer 725
n03954731 plane, carpenter's plane, woodworking plane 726
n03956157 planetarium 727
n03958227 plastic bag 728
n03961711 plate rack 729
n03967562 plow, plough 730
n03970156 plunger, plumber's helper 731
n03976467 Polaroid camera, Polaroid Land camera 732
n03976657 pole 733
n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria 734
n03980874 poncho 735
n03982430 pool table, billiard table, snooker table 736
n03983396 pop bottle, soda bottle 737
n03991062 pot, flowerpot 738
n03992509 potter's wheel 739
n03995372 power drill 740
n03998194 prayer rug, prayer mat 741
n04004767 printer 742
n04005630 prison, prison house 743
n04008634 projectile, missile 744
n04009552 projector 745
n04019541 puck, hockey puck 746
n04023962 punching bag, punch bag, punching ball, punchball 747
n04026417 purse 748
n04033901 quill, quill pen 749
n04033995 quilt, comforter, comfort, puff 750
n04037443 racer, race car, racing car 751
n04039381 racket, racquet 752
n04040759 radiator 753
n04041544 radio, wireless 754
n04044716 radio telescope, radio reflector 755
n04049303 rain barrel 756
n04065272 recreational vehicle, RV, R.V. 757
n04067472 reel 758
n04069434 reflex camera 759
n04070727 refrigerator, icebox 760
n04074963 remote control, remote 761
n04081281 restaurant, eating house, eating place, eatery 762
n04086273 revolver, six-gun, six-shooter 763
n04090263 rifle 764
n04099969 rocking chair, rocker 765
n04111531 rotisserie 766
n04116512 rubber eraser, rubber, pencil eraser 767
n04118538 rugby ball 768
n04118776 rule, ruler 769
n04120489 running shoe 770
n04125021 safe 771
n04127249 safety pin 772
n04131690 saltshaker, salt shaker 773
n04133789 sandal 774
n04136333 sarong 775
n04141076 sax, saxophone 776
n04141327 scabbard 777
n04141975 scale, weighing machine 778
n04146614 school bus 779
n04147183 schooner 780
n04149813 scoreboard 781
n04152593 screen, CRT screen 782
n04153751 screw 783
n04154565 screwdriver 784
n04162706 seat belt, seatbelt 785
n04179913 sewing machine 786
n04192698 shield, buckler 787
n04200800 shoe shop, shoe-shop, shoe store 788
n04201297 shoji 789
n04204238 shopping basket 790
n04204347 shopping cart 791
n04208210 shovel 792
n04209133 shower cap 793
n04209239 shower curtain 794
n04228054 ski 795
n04229816 ski mask 796
n04235860 sleeping bag 797
n04238763 slide rule, slipstick 798
n04239074 sliding door 799
n04243546 slot, one-armed bandit 800
n04251144 snorkel 801
n04252077 snowmobile 802
n04252225 snowplow, snowplough 803
n04254120 soap dispenser 804
n04254680 soccer ball 805
n04254777 sock 806
n04258138 solar dish, solar collector, solar furnace 807
n04259630 sombrero 808
n04263257 soup bowl 809
n04264628 space bar 810
n04265275 space heater 811
n04266014 space shuttle 812
n04270147 spatula 813
n04273569 speedboat 814
n04275548 spider web, spider's web 815
n04277352 spindle 816
n04285008 sports car, sport car 817
n04286575 spotlight, spot 818
n04296562 stage 819
n04310018 steam locomotive 820
n04311004 steel arch bridge 821
n04311174 steel drum 822
n04317175 stethoscope 823
n04325704 stole 824
n04326547 stone wall 825
n04328186 stopwatch, stop watch 826
n04330267 stove 827
n04332243 strainer 828
n04335435 streetcar, tram, tramcar, trolley, trolley car 829
n04336792 stretcher 830
n04344873 studio couch, day bed 831
n04346328 stupa, tope 832
n04347754 submarine, pigboat, sub, U-boat 833
n04350905 suit, suit of clothes 834
n04355338 sundial 835
n04355933 sunglass 836
n04356056 sunglasses, dark glasses, shades 837
n04357314 sunscreen, sunblock, sun blocker 838
n04366367 suspension bridge 839
n04367480 swab, swob, mop 840
n04370456 sweatshirt 841
n04371430 swimming trunks, bathing trunks 842
n04371774 swing 843
n04372370 switch, electric switch, electrical switch 844
n04376876 syringe 845
n04380533 table lamp 846
n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle 847
n04392985 tape player 848
n04398044 teapot 849
n04399382 teddy, teddy bear 850
n04404412 television, television system 851
n04409515 tennis ball 852
n04417672 thatch, thatched roof 853
n04418357 theater curtain, theatre curtain 854
n04423845 thimble 855
n04428191 thresher, thrasher, threshing machine 856
n04429376 throne 857
n04435653 tile roof 858
n04442312 toaster 859
n04443257 tobacco shop, tobacconist shop, tobacconist 860
n04447861 toilet seat 861
n04456115 torch 862
n04458633 totem pole 863
n04461696 tow truck, tow car, wrecker 864
n04462240 toyshop 865
n04465501 tractor 866
n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi 867
n04476259 tray 868
n04479046 trench coat 869
n04482393 tricycle, trike, velocipede 870
n04483307 trimaran 871
n04485082 tripod 872
n04486054 triumphal arch 873
n04487081 trolleybus, trolley coach, trackless trolley 874
n04487394 trombone 875
n04493381 tub, vat 876
n04501370 turnstile 877
n04505470 typewriter keyboard 878
n04507155 umbrella 879
n04509417 unicycle, monocycle 880
n04515003 upright, upright piano 881
n04517823 vacuum, vacuum cleaner 882
n04522168 vase 883
n04523525 vault 884
n04525038 velvet 885
n04525305 vending machine 886
n04532106 vestment 887
n04532670 viaduct 888
n04536866 violin, fiddle 889
n04540053 volleyball 890
n04542943 waffle iron 891
n04548280 wall clock 892
n04548362 wallet, billfold, notecase, pocketbook 893
n04550184 wardrobe, closet, press 894
n04552348 warplane, military plane 895
n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin 896
n04554684 washer, automatic washer, washing machine 897
n04557648 water bottle 898
n04560804 water jug 899
n04562935 water tower 900
n04579145 whiskey jug 901
n04579432 whistle 902
n04584207 wig 903
n04589890 window screen 904
n04590129 window shade 905
n04591157 Windsor tie 906
n04591713 wine bottle 907
n04592741 wing 908
n04596742 wok 909
n04597913 wooden spoon 910
n04599235 wool, woolen, woollen 911
n04604644 worm fence, snake fence, snake-rail fence, Virginia fence 912
n04606251 wreck 913
n04612504 yawl 914
n04613696 yurt 915
n06359193 web site, website, internet site, site 916
n06596364 comic book 917
n06785654 crossword puzzle, crossword 918
n06794110 street sign 919
n06874185 traffic light, traffic signal, stoplight 920
n07248320 book jacket, dust cover, dust jacket, dust wrapper 921
n07565083 menu 922
n07579787 plate 923
n07583066 guacamole 924
n07584110 consomme 925
n07590611 hot pot, hotpot 926
n07613480 trifle 927
n07614500 ice cream, icecream 928
n07615774 ice lolly, lolly, lollipop, popsicle 929
n07684084 French loaf 930
n07693725 bagel, beigel 931
n07695742 pretzel 932
n07697313 cheeseburger 933
n07697537 hotdog, hot dog, red hot 934
n07711569 mashed potato 935
n07714571 head cabbage 936
n07714990 broccoli 937
n07715103 cauliflower 938
n07716358 zucchini, courgette 939
n07716906 spaghetti squash 940
n07717410 acorn squash 941
n07717556 butternut squash 942
n07718472 cucumber, cuke 943
n07718747 artichoke, globe artichoke 944
n07720875 bell pepper 945
n07730033 cardoon 946
n07734744 mushroom 947
n07742313 Granny Smith 948
n07745940 strawberry 949
n07747607 orange 950
n07749582 lemon 951
n07753113 fig 952
n07753275 pineapple, ananas 953
n07753592 banana 954
n07754684 jackfruit, jak, jack 955
n07760859 custard apple 956
n07768694 pomegranate 957
n07802026 hay 958
n07831146 carbonara 959
n07836838 chocolate sauce, chocolate syrup 960
n07860988 dough 961
n07871810 meat loaf, meatloaf 962
n07873807 pizza, pizza pie 963
n07875152 potpie 964
n07880968 burrito 965
n07892512 red wine 966
n07920052 espresso 967
n07930864 cup 968
n07932039 eggnog 969
n09193705 alp 970
n09229709 bubble 971
n09246464 cliff, drop, drop-off 972
n09256479 coral reef 973
n09288635 geyser 974
n09332890 lakeside, lakeshore 975
n09399592 promontory, headland, head, foreland 976
n09421951 sandbar, sand bar 977
n09428293 seashore, coast, seacoast, sea-coast 978
n09468604 valley, vale 979
n09472597 volcano 980
n09835506 ballplayer, baseball player 981
n10148035 groom, bridegroom 982
n10565667 scuba diver 983
n11879895 rapeseed 984
n11939491 daisy 985
n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum 986
n12144580 corn 987
n12267677 acorn 988
n12620546 hip, rose hip, rosehip 989
n12768682 buckeye, horse chestnut, conker 990
n12985857 coral fungus 991
n12998815 agaric 992
n13037406 gyromitra 993
n13040303 stinkhorn, carrion fungus 994
n13044778 earthstar 995
n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa 996
n13054560 bolete 997
n13133613 ear, spike, capitulum 998
n15075141 toilet tissue, toilet paper, bathroom tissue 999
/a/airfield 1000
/a/airplane_cabin 1001
/a/airport_terminal 1002
/a/alcove 1003
/a/alley 1004
/a/amphitheater 1005
/a/amusement_arcade 1006
/a/amusement_park 1007
/a/apartment_building/outdoor 1008
/a/aquarium 1009
/a/aqueduct 1010
/a/arcade 1011
/a/arch 1012
/a/archaelogical_excavation 1013
/a/archive 1014
/a/arena/hockey 1015
/a/arena/performance 1016
/a/arena/rodeo 1017
/a/army_base 1018
/a/art_gallery 1019
/a/art_school 1020
/a/art_studio 1021
/a/artists_loft 1022
/a/assembly_line 1023
/a/athletic_field/outdoor 1024
/a/atrium/public 1025
/a/attic 1026
/a/auditorium 1027
/a/auto_factory 1028
/a/auto_showroom 1029
/b/badlands 1030
/b/bakery/shop 1031
/b/balcony/exterior 1032
/b/balcony/interior 1033
/b/ball_pit 1034
/b/ballroom 1035
/b/bamboo_forest 1036
/b/bank_vault 1037
/b/banquet_hall 1038
/b/bar 1039
/b/barn 1040
/b/barndoor 1041
/b/baseball_field 1042
/b/basement 1043
/b/basketball_court/indoor 1044
/b/bathroom 1045
/b/bazaar/indoor 1046
/b/bazaar/outdoor 1047
/b/beach 1048
/b/beach_house 1049
/b/beauty_salon 1050
/b/bedchamber 1051
/b/bedroom 1052
/b/beer_garden 1053
/b/beer_hall 1054
/b/berth 1055
/b/biology_laboratory 1056
/b/boardwalk 1057
/b/boat_deck 1058
/b/boathouse 1059
/b/bookstore 1060
/b/booth/indoor 1061
/b/botanical_garden 1062
/b/bow_window/indoor 1063
/b/bowling_alley 1064
/b/boxing_ring 1065
/b/bridge 1066
/b/building_facade 1067
/b/bullring 1068
/b/burial_chamber 1069
/b/bus_interior 1070
/b/bus_station/indoor 1071
/b/butchers_shop 1072
/b/butte 1073
/c/cabin/outdoor 1074
/c/cafeteria 1075
/c/campsite 1076
/c/campus 1077
/c/canal/natural 1078
/c/canal/urban 1079
/c/candy_store 1080
/c/canyon 1081
/c/car_interior 1082
/c/carrousel 1083
/c/castle 1084
/c/catacomb 1085
/c/cemetery 1086
/c/chalet 1087
/c/chemistry_lab 1088
/c/childs_room 1089
/c/church/indoor 1090
/c/church/outdoor 1091
/c/classroom 1092
/c/clean_room 1093
/c/cliff 1094
/c/closet 1095
/c/clothing_store 1096
/c/coast 1097
/c/cockpit 1098
/c/coffee_shop 1099
/c/computer_room 1100
/c/conference_center 1101
/c/conference_room 1102
/c/construction_site 1103
/c/corn_field 1104
/c/corral 1105
/c/corridor 1106
/c/cottage 1107
/c/courthouse 1108
/c/courtyard 1109
/c/creek 1110
/c/crevasse 1111
/c/crosswalk 1112
/d/dam 1113
/d/delicatessen 1114
/d/department_store 1115
/d/desert/sand 1116
/d/desert/vegetation 1117
/d/desert_road 1118
/d/diner/outdoor 1119
/d/dining_hall 1120
/d/dining_room 1121
/d/discotheque 1122
/d/doorway/outdoor 1123
/d/dorm_room 1124
/d/downtown 1125
/d/dressing_room 1126
/d/driveway 1127
/d/drugstore 1128
/e/elevator/door 1129
/e/elevator_lobby 1130
/e/elevator_shaft 1131
/e/embassy 1132
/e/engine_room 1133
/e/entrance_hall 1134
/e/escalator/indoor 1135
/e/excavation 1136
/f/fabric_store 1137
/f/farm 1138
/f/fastfood_restaurant 1139
/f/field/cultivated 1140
/f/field/wild 1141
/f/field_road 1142
/f/fire_escape 1143
/f/fire_station 1144
/f/fishpond 1145
/f/flea_market/indoor 1146
/f/florist_shop/indoor 1147
/f/food_court 1148
/f/football_field 1149
/f/forest/broadleaf 1150
/f/forest_path 1151
/f/forest_road 1152
/f/formal_garden 1153
/f/fountain 1154
/g/galley 1155
/g/garage/indoor 1156
/g/garage/outdoor 1157
/g/gas_station 1158
/g/gazebo/exterior 1159
/g/general_store/indoor 1160
/g/general_store/outdoor 1161
/g/gift_shop 1162
/g/glacier 1163
/g/golf_course 1164
/g/greenhouse/indoor 1165
/g/greenhouse/outdoor 1166
/g/grotto 1167
/g/gymnasium/indoor 1168
/h/hangar/indoor 1169
/h/hangar/outdoor 1170
/h/harbor 1171
/h/hardware_store 1172
/h/hayfield 1173
/h/heliport 1174
/h/highway 1175
/h/home_office 1176
/h/home_theater 1177
/h/hospital 1178
/h/hospital_room 1179
/h/hot_spring 1180
/h/hotel/outdoor 1181
/h/hotel_room 1182
/h/house 1183
/h/hunting_lodge/outdoor 1184
/i/ice_cream_parlor 1185
/i/ice_floe 1186
/i/ice_shelf 1187
/i/ice_skating_rink/indoor 1188
/i/ice_skating_rink/outdoor 1189
/i/iceberg 1190
/i/igloo 1191
/i/industrial_area 1192
/i/inn/outdoor 1193
/i/islet 1194
/j/jacuzzi/indoor 1195
/j/jail_cell 1196
/j/japanese_garden 1197
/j/jewelry_shop 1198
/j/junkyard 1199
/k/kasbah 1200
/k/kennel/outdoor 1201
/k/kindergarden_classroom 1202
/k/kitchen 1203
/l/lagoon 1204
/l/lake/natural 1205
/l/landfill 1206
/l/landing_deck 1207
/l/laundromat 1208
/l/lawn 1209
/l/lecture_room 1210
/l/legislative_chamber 1211
/l/library/indoor 1212
/l/library/outdoor 1213
/l/lighthouse 1214
/l/living_room 1215
/l/loading_dock 1216
/l/lobby 1217
/l/lock_chamber 1218
/l/locker_room 1219
/m/mansion 1220
/m/manufactured_home 1221
/m/market/indoor 1222
/m/market/outdoor 1223
/m/marsh 1224
/m/martial_arts_gym 1225
/m/mausoleum 1226
/m/medina 1227
/m/mezzanine 1228
/m/moat/water 1229
/m/mosque/outdoor 1230
/m/motel 1231
/m/mountain 1232
/m/mountain_path 1233
/m/mountain_snowy 1234
/m/movie_theater/indoor 1235
/m/museum/indoor 1236
/m/museum/outdoor 1237
/m/music_studio 1238
/n/natural_history_museum 1239
/n/nursery 1240
/n/nursing_home 1241
/o/oast_house 1242
/o/ocean 1243
/o/office 1244
/o/office_building 1245
/o/office_cubicles 1246
/o/oilrig 1247
/o/operating_room 1248
/o/orchard 1249
/o/orchestra_pit 1250
/p/pagoda 1251
/p/palace 1252
/p/pantry 1253
/p/park 1254
/p/parking_garage/indoor 1255
/p/parking_garage/outdoor 1256
/p/parking_lot 1257
/p/pasture 1258
/p/patio 1259
/p/pavilion 1260
/p/pet_shop 1261
/p/pharmacy 1262
/p/phone_booth 1263
/p/physics_laboratory 1264
/p/picnic_area 1265
/p/pier 1266
/p/pizzeria 1267
/p/playground 1268
/p/playroom 1269
/p/plaza 1270
/p/pond 1271
/p/porch 1272
/p/promenade 1273
/p/pub/indoor 1274
/r/racecourse 1275
/r/raceway 1276
/r/raft 1277
/r/railroad_track 1278
/r/rainforest 1279
/r/reception 1280
/r/recreation_room 1281
/r/repair_shop 1282
/r/residential_neighborhood 1283
/r/restaurant 1284
/r/restaurant_kitchen 1285
/r/restaurant_patio 1286
/r/rice_paddy 1287
/r/river 1288
/r/rock_arch 1289
/r/roof_garden 1290
/r/rope_bridge 1291
/r/ruin 1292
/r/runway 1293
/s/sandbox 1294
/s/sauna 1295
/s/schoolhouse 1296
/s/science_museum 1297
/s/server_room 1298
/s/shed 1299
/s/shoe_shop 1300
/s/shopfront 1301
/s/shopping_mall/indoor 1302
/s/shower 1303
/s/ski_resort 1304
/s/ski_slope 1305
/s/sky 1306
/s/skyscraper 1307
/s/slum 1308
/s/snowfield 1309
/s/soccer_field 1310
/s/stable 1311
/s/stadium/baseball 1312
/s/stadium/football 1313
/s/stadium/soccer 1314
/s/stage/indoor 1315
/s/stage/outdoor 1316
/s/staircase 1317
/s/storage_room 1318
/s/street 1319
/s/subway_station/platform 1320
/s/supermarket 1321
/s/sushi_bar 1322
/s/swamp 1323
/s/swimming_hole 1324
/s/swimming_pool/indoor 1325
/s/swimming_pool/outdoor 1326
/s/synagogue/outdoor 1327
/t/television_room 1328
/t/television_studio 1329
/t/temple/asia 1330
/t/throne_room 1331
/t/ticket_booth 1332
/t/topiary_garden 1333
/t/tower 1334
/t/toyshop 1335
/t/train_interior 1336
/t/train_station/platform 1337
/t/tree_farm 1338
/t/tree_house 1339
/t/trench 1340
/t/tundra 1341
/u/underwater/ocean_deep 1342
/u/utility_room 1343
/v/valley 1344
/v/vegetable_garden 1345
/v/veterinarians_office 1346
/v/viaduct 1347
/v/village 1348
/v/vineyard 1349
/v/volcano 1350
/v/volleyball_court/outdoor 1351
/w/waiting_room 1352
/w/water_park 1353
/w/water_tower 1354
/w/waterfall 1355
/w/watering_hole 1356
/w/wave 1357
/w/wet_bar 1358
/w/wheat_field 1359
/w/wind_farm 1360
/w/windmill 1361
/y/yard 1362
/y/youth_hostel 1363
/z/zen_garden 1364
================================================
FILE: categories_places365.txt
================================================
/a/airfield 0
/a/airplane_cabin 1
/a/airport_terminal 2
/a/alcove 3
/a/alley 4
/a/amphitheater 5
/a/amusement_arcade 6
/a/amusement_park 7
/a/apartment_building/outdoor 8
/a/aquarium 9
/a/aqueduct 10
/a/arcade 11
/a/arch 12
/a/archaelogical_excavation 13
/a/archive 14
/a/arena/hockey 15
/a/arena/performance 16
/a/arena/rodeo 17
/a/army_base 18
/a/art_gallery 19
/a/art_school 20
/a/art_studio 21
/a/artists_loft 22
/a/assembly_line 23
/a/athletic_field/outdoor 24
/a/atrium/public 25
/a/attic 26
/a/auditorium 27
/a/auto_factory 28
/a/auto_showroom 29
/b/badlands 30
/b/bakery/shop 31
/b/balcony/exterior 32
/b/balcony/interior 33
/b/ball_pit 34
/b/ballroom 35
/b/bamboo_forest 36
/b/bank_vault 37
/b/banquet_hall 38
/b/bar 39
/b/barn 40
/b/barndoor 41
/b/baseball_field 42
/b/basement 43
/b/basketball_court/indoor 44
/b/bathroom 45
/b/bazaar/indoor 46
/b/bazaar/outdoor 47
/b/beach 48
/b/beach_house 49
/b/beauty_salon 50
/b/bedchamber 51
/b/bedroom 52
/b/beer_garden 53
/b/beer_hall 54
/b/berth 55
/b/biology_laboratory 56
/b/boardwalk 57
/b/boat_deck 58
/b/boathouse 59
/b/bookstore 60
/b/booth/indoor 61
/b/botanical_garden 62
/b/bow_window/indoor 63
/b/bowling_alley 64
/b/boxing_ring 65
/b/bridge 66
/b/building_facade 67
/b/bullring 68
/b/burial_chamber 69
/b/bus_interior 70
/b/bus_station/indoor 71
/b/butchers_shop 72
/b/butte 73
/c/cabin/outdoor 74
/c/cafeteria 75
/c/campsite 76
/c/campus 77
/c/canal/natural 78
/c/canal/urban 79
/c/candy_store 80
/c/canyon 81
/c/car_interior 82
/c/carrousel 83
/c/castle 84
/c/catacomb 85
/c/cemetery 86
/c/chalet 87
/c/chemistry_lab 88
/c/childs_room 89
/c/church/indoor 90
/c/church/outdoor 91
/c/classroom 92
/c/clean_room 93
/c/cliff 94
/c/closet 95
/c/clothing_store 96
/c/coast 97
/c/cockpit 98
/c/coffee_shop 99
/c/computer_room 100
/c/conference_center 101
/c/conference_room 102
/c/construction_site 103
/c/corn_field 104
/c/corral 105
/c/corridor 106
/c/cottage 107
/c/courthouse 108
/c/courtyard 109
/c/creek 110
/c/crevasse 111
/c/crosswalk 112
/d/dam 113
/d/delicatessen 114
/d/department_store 115
/d/desert/sand 116
/d/desert/vegetation 117
/d/desert_road 118
/d/diner/outdoor 119
/d/dining_hall 120
/d/dining_room 121
/d/discotheque 122
/d/doorway/outdoor 123
/d/dorm_room 124
/d/downtown 125
/d/dressing_room 126
/d/driveway 127
/d/drugstore 128
/e/elevator/door 129
/e/elevator_lobby 130
/e/elevator_shaft 131
/e/embassy 132
/e/engine_room 133
/e/entrance_hall 134
/e/escalator/indoor 135
/e/excavation 136
/f/fabric_store 137
/f/farm 138
/f/fastfood_restaurant 139
/f/field/cultivated 140
/f/field/wild 141
/f/field_road 142
/f/fire_escape 143
/f/fire_station 144
/f/fishpond 145
/f/flea_market/indoor 146
/f/florist_shop/indoor 147
/f/food_court 148
/f/football_field 149
/f/forest/broadleaf 150
/f/forest_path 151
/f/forest_road 152
/f/formal_garden 153
/f/fountain 154
/g/galley 155
/g/garage/indoor 156
/g/garage/outdoor 157
/g/gas_station 158
/g/gazebo/exterior 159
/g/general_store/indoor 160
/g/general_store/outdoor 161
/g/gift_shop 162
/g/glacier 163
/g/golf_course 164
/g/greenhouse/indoor 165
/g/greenhouse/outdoor 166
/g/grotto 167
/g/gymnasium/indoor 168
/h/hangar/indoor 169
/h/hangar/outdoor 170
/h/harbor 171
/h/hardware_store 172
/h/hayfield 173
/h/heliport 174
/h/highway 175
/h/home_office 176
/h/home_theater 177
/h/hospital 178
/h/hospital_room 179
/h/hot_spring 180
/h/hotel/outdoor 181
/h/hotel_room 182
/h/house 183
/h/hunting_lodge/outdoor 184
/i/ice_cream_parlor 185
/i/ice_floe 186
/i/ice_shelf 187
/i/ice_skating_rink/indoor 188
/i/ice_skating_rink/outdoor 189
/i/iceberg 190
/i/igloo 191
/i/industrial_area 192
/i/inn/outdoor 193
/i/islet 194
/j/jacuzzi/indoor 195
/j/jail_cell 196
/j/japanese_garden 197
/j/jewelry_shop 198
/j/junkyard 199
/k/kasbah 200
/k/kennel/outdoor 201
/k/kindergarden_classroom 202
/k/kitchen 203
/l/lagoon 204
/l/lake/natural 205
/l/landfill 206
/l/landing_deck 207
/l/laundromat 208
/l/lawn 209
/l/lecture_room 210
/l/legislative_chamber 211
/l/library/indoor 212
/l/library/outdoor 213
/l/lighthouse 214
/l/living_room 215
/l/loading_dock 216
/l/lobby 217
/l/lock_chamber 218
/l/locker_room 219
/m/mansion 220
/m/manufactured_home 221
/m/market/indoor 222
/m/market/outdoor 223
/m/marsh 224
/m/martial_arts_gym 225
/m/mausoleum 226
/m/medina 227
/m/mezzanine 228
/m/moat/water 229
/m/mosque/outdoor 230
/m/motel 231
/m/mountain 232
/m/mountain_path 233
/m/mountain_snowy 234
/m/movie_theater/indoor 235
/m/museum/indoor 236
/m/museum/outdoor 237
/m/music_studio 238
/n/natural_history_museum 239
/n/nursery 240
/n/nursing_home 241
/o/oast_house 242
/o/ocean 243
/o/office 244
/o/office_building 245
/o/office_cubicles 246
/o/oilrig 247
/o/operating_room 248
/o/orchard 249
/o/orchestra_pit 250
/p/pagoda 251
/p/palace 252
/p/pantry 253
/p/park 254
/p/parking_garage/indoor 255
/p/parking_garage/outdoor 256
/p/parking_lot 257
/p/pasture 258
/p/patio 259
/p/pavilion 260
/p/pet_shop 261
/p/pharmacy 262
/p/phone_booth 263
/p/physics_laboratory 264
/p/picnic_area 265
/p/pier 266
/p/pizzeria 267
/p/playground 268
/p/playroom 269
/p/plaza 270
/p/pond 271
/p/porch 272
/p/promenade 273
/p/pub/indoor 274
/r/racecourse 275
/r/raceway 276
/r/raft 277
/r/railroad_track 278
/r/rainforest 279
/r/reception 280
/r/recreation_room 281
/r/repair_shop 282
/r/residential_neighborhood 283
/r/restaurant 284
/r/restaurant_kitchen 285
/r/restaurant_patio 286
/r/rice_paddy 287
/r/river 288
/r/rock_arch 289
/r/roof_garden 290
/r/rope_bridge 291
/r/ruin 292
/r/runway 293
/s/sandbox 294
/s/sauna 295
/s/schoolhouse 296
/s/science_museum 297
/s/server_room 298
/s/shed 299
/s/shoe_shop 300
/s/shopfront 301
/s/shopping_mall/indoor 302
/s/shower 303
/s/ski_resort 304
/s/ski_slope 305
/s/sky 306
/s/skyscraper 307
/s/slum 308
/s/snowfield 309
/s/soccer_field 310
/s/stable 311
/s/stadium/baseball 312
/s/stadium/football 313
/s/stadium/soccer 314
/s/stage/indoor 315
/s/stage/outdoor 316
/s/staircase 317
/s/storage_room 318
/s/street 319
/s/subway_station/platform 320
/s/supermarket 321
/s/sushi_bar 322
/s/swamp 323
/s/swimming_hole 324
/s/swimming_pool/indoor 325
/s/swimming_pool/outdoor 326
/s/synagogue/outdoor 327
/t/television_room 328
/t/television_studio 329
/t/temple/asia 330
/t/throne_room 331
/t/ticket_booth 332
/t/topiary_garden 333
/t/tower 334
/t/toyshop 335
/t/train_interior 336
/t/train_station/platform 337
/t/tree_farm 338
/t/tree_house 339
/t/trench 340
/t/tundra 341
/u/underwater/ocean_deep 342
/u/utility_room 343
/v/valley 344
/v/vegetable_garden 345
/v/veterinarians_office 346
/v/viaduct 347
/v/village 348
/v/vineyard 349
/v/volcano 350
/v/volleyball_court/outdoor 351
/w/waiting_room 352
/w/water_park 353
/w/water_tower 354
/w/waterfall 355
/w/watering_hole 356
/w/wave 357
/w/wet_bar 358
/w/wheat_field 359
/w/wind_farm 360
/w/windmill 361
/y/yard 362
/y/youth_hostel 363
/z/zen_garden 364
================================================
FILE: places365_class_index.json
================================================
{
"0": [
"n0",
"airfield"
],
"1": [
"n1",
"airplane_cabin"
],
"2": [
"n2",
"airport_terminal"
],
"3": [
"n3",
"alcove"
],
"4": [
"n4",
"alley"
],
"5": [
"n5",
"amphitheater"
],
"6": [
"n6",
"amusement_arcade"
],
"7": [
"n7",
"amusement_park"
],
"8": [
"n8",
"apartment_building/outdoor"
],
"9": [
"n9",
"aquarium"
],
"10": [
"n10",
"aqueduct"
],
"11": [
"n11",
"arcade"
],
"12": [
"n12",
"arch"
],
"13": [
"n13",
"archaelogical_excavation"
],
"14": [
"n14",
"archive"
],
"15": [
"n15",
"hockey"
],
"16": [
"n16",
"performance"
],
"17": [
"n17",
"rodeo"
],
"18": [
"n18",
"army_base"
],
"19": [
"n19",
"art_gallery"
],
"20": [
"n20",
"art_school"
],
"21": [
"n21",
"art_studio"
],
"22": [
"n22",
"artists_loft"
],
"23": [
"n23",
"assembly_line"
],
"24": [
"n24",
"athletic_field/outdoor"
],
"25": [
"n25",
"atrium/public"
],
"26": [
"n26",
"attic"
],
"27": [
"n27",
"auditorium"
],
"28": [
"n28",
"auto_factory"
],
"29": [
"n29",
"auto_showroom"
],
"30": [
"n30",
"badlands"
],
"31": [
"n31",
"bakery/shop"
],
"32": [
"n32",
"balcony/exterior"
],
"33": [
"n33",
"balcony/interior"
],
"34": [
"n34",
"ball_pit"
],
"35": [
"n35",
"ballroom"
],
"36": [
"n36",
"bamboo_forest"
],
"37": [
"n37",
"bank_vault"
],
"38": [
"n38",
"banquet_hall"
],
"39": [
"n39",
"bar"
],
"40": [
"n40",
"barn"
],
"41": [
"n41",
"barndoor"
],
"42": [
"n42",
"baseball_field"
],
"43": [
"n43",
"basement"
],
"44": [
"n44",
"basketball_court/indoor"
],
"45": [
"n45",
"bathroom"
],
"46": [
"n46",
"bazaar/indoor"
],
"47": [
"n47",
"bazaar/outdoor"
],
"48": [
"n48",
"beach"
],
"49": [
"n49",
"beach_house"
],
"50": [
"n50",
"beauty_salon"
],
"51": [
"n51",
"bedchamber"
],
"52": [
"n52",
"bedroom"
],
"53": [
"n53",
"beer_garden"
],
"54": [
"n54",
"beer_hall"
],
"55": [
"n55",
"berth"
],
"56": [
"n56",
"biology_laboratory"
],
"57": [
"n57",
"boardwalk"
],
"58": [
"n58",
"boat_deck"
],
"59": [
"n59",
"boathouse"
],
"60": [
"n60",
"bookstore"
],
"61": [
"n61",
"booth/indoor"
],
"62": [
"n62",
"botanical_garden"
],
"63": [
"n63",
"bow_window/indoor"
],
"64": [
"n64",
"bowling_alley"
],
"65": [
"n65",
"boxing_ring"
],
"66": [
"n66",
"bridge"
],
"67": [
"n67",
"building_facade"
],
"68": [
"n68",
"bullring"
],
"69": [
"n69",
"burial_chamber"
],
"70": [
"n70",
"bus_interior"
],
"71": [
"n71",
"bus_station/indoor"
],
"72": [
"n72",
"butchers_shop"
],
"73": [
"n73",
"butte"
],
"74": [
"n74",
"cabin/outdoor"
],
"75": [
"n75",
"cafeteria"
],
"76": [
"n76",
"campsite"
],
"77": [
"n77",
"campus"
],
"78": [
"n78",
"canal/natural"
],
"79": [
"n79",
"canal/urban"
],
"80": [
"n80",
"candy_store"
],
"81": [
"n81",
"canyon"
],
"82": [
"n82",
"car_interior"
],
"83": [
"n83",
"carrousel"
],
"84": [
"n84",
"castle"
],
"85": [
"n85",
"catacomb"
],
"86": [
"n86",
"cemetery"
],
"87": [
"n87",
"chalet"
],
"88": [
"n88",
"chemistry_lab"
],
"89": [
"n89",
"childs_room"
],
"90": [
"n90",
"church/indoor"
],
"91": [
"n91",
"church/outdoor"
],
"92": [
"n92",
"classroom"
],
"93": [
"n93",
"clean_room"
],
"94": [
"n94",
"cliff"
],
"95": [
"n95",
"closet"
],
"96": [
"n96",
"clothing_store"
],
"97": [
"n97",
"coast"
],
"98": [
"n98",
"cockpit"
],
"99": [
"n99",
"coffee_shop"
],
"100": [
"n100",
"computer_room"
],
"101": [
"n101",
"conference_center"
],
"102": [
"n102",
"conference_room"
],
"103": [
"n103",
"construction_site"
],
"104": [
"n104",
"corn_field"
],
"105": [
"n105",
"corral"
],
"106": [
"n106",
"corridor"
],
"107": [
"n107",
"cottage"
],
"108": [
"n108",
"courthouse"
],
"109": [
"n109",
"courtyard"
],
"110": [
"n110",
"creek"
],
"111": [
"n111",
"crevasse"
],
"112": [
"n112",
"crosswalk"
],
"113": [
"n113",
"dam"
],
"114": [
"n114",
"delicatessen"
],
"115": [
"n115",
"department_store"
],
"116": [
"n116",
"sand"
],
"117": [
"n117",
"vegetation"
],
"118": [
"n118",
"desert_road"
],
"119": [
"n119",
"diner/outdoor"
],
"120": [
"n120",
"dining_hall"
],
"121": [
"n121",
"dining_room"
],
"122": [
"n122",
"discotheque"
],
"123": [
"n123",
"doorway/outdoor"
],
"124": [
"n124",
"dorm_room"
],
"125": [
"n125",
"downtown"
],
"126": [
"n126",
"dressing_room"
],
"127": [
"n127",
"driveway"
],
"128": [
"n128",
"drugstore"
],
"129": [
"n129",
"elevator/door"
],
"130": [
"n130",
"elevator_lobby"
],
"131": [
"n131",
"elevator_shaft"
],
"132": [
"n132",
"embassy"
],
"133": [
"n133",
"engine_room"
],
"134": [
"n134",
"entrance_hall"
],
"135": [
"n135",
"escalator/indoor"
],
"136": [
"n136",
"excavation"
],
"137": [
"n137",
"fabric_store"
],
"138": [
"n138",
"farm"
],
"139": [
"n139",
"fastfood_restaurant"
],
"140": [
"n140",
"field/cultivated"
],
"141": [
"n141",
"field/wild "
],
"142": [
"n142",
"field_road"
],
"143": [
"n143",
"fire_escape"
],
"144": [
"n144",
"fire_station"
],
"145": [
"n145",
"fishpond"
],
"146": [
"n146",
"flea_market/indoor"
],
"147": [
"n147",
"florist_shop/indoor"
],
"148": [
"n148",
"food_court"
],
"149": [
"n149",
"football_field"
],
"150": [
"n150",
"forest/broadleaf"
],
"151": [
"n151",
"forest_path"
],
"152": [
"n152",
"forest_road"
],
"153": [
"n153",
"formal_garden"
],
"154": [
"n154",
"fountain"
],
"155": [
"n155",
"galley"
],
"156": [
"n156",
"garage/indoor"
],
"157": [
"n157",
"garage/outdoor"
],
"158": [
"n158",
"gas_station"
],
"159": [
"n159",
"gazebo/exterior"
],
"160": [
"n160",
"general_store/indoor"
],
"161": [
"n161",
"general_store/outdoor"
],
"162": [
"n162",
"gift_shop"
],
"163": [
"n163",
"glacier"
],
"164": [
"n164",
"golf_course"
],
"165": [
"n165",
"greenhouse/indoor"
],
"166": [
"n166",
"greenhouse/outdoor"
],
"167": [
"n167",
"grotto"
],
"168": [
"n168",
"gymnasium/indoor"
],
"169": [
"n169",
"hangar/indoor"
],
"170": [
"n170",
"hangar/outdoor"
],
"171": [
"n171",
"harbor"
],
"172": [
"n172",
"hardware_store"
],
"173": [
"n173",
"hayfield"
],
"174": [
"n174",
"heliport"
],
"175": [
"n175",
"highway"
],
"176": [
"n176",
"home_office"
],
"177": [
"n177",
"home_theater"
],
"178": [
"n178",
"hospital"
],
"179": [
"n179",
"hospital_room"
],
"180": [
"n180",
"hot_spring"
],
"181": [
"n181",
"hotel/outdoor"
],
"182": [
"n182",
"hotel_room"
],
"183": [
"n183",
"house"
],
"184": [
"n184",
"hunting_lodge/outdoor"
],
"185": [
"n185",
"ice_cream_parlor"
],
"186": [
"n186",
"ice_floe"
],
"187": [
"n187",
"ice_shelf"
],
"188": [
"n188",
"ice_skating_rink/indoor"
],
"189": [
"n189",
"ice_skating_rink/outdoor"
],
"190": [
"n190",
"iceberg"
],
"191": [
"n191",
"igloo"
],
"192": [
"n192",
"industrial_area"
],
"193": [
"n193",
"inn/outdoor"
],
"194": [
"n194",
"islet"
],
"195": [
"n195",
"jacuzzi/indoor"
],
"196": [
"n196",
"jail_cell"
],
"197": [
"n197",
"japanese_garden"
],
"198": [
"n198",
"jewelry_shop"
],
"199": [
"n199",
"junkyard"
],
"200": [
"n200",
"kasbah"
],
"201": [
"n201",
"kennel/outdoor"
],
"202": [
"n202",
"kindergarden_classroom"
],
"203": [
"n203",
"kitchen"
],
"204": [
"n204",
"lagoon"
],
"205": [
"n205",
"lake/natural"
],
"206": [
"n206",
"landfill"
],
"207": [
"n207",
"landing_deck"
],
"208": [
"n208",
"laundromat"
],
"209": [
"n209",
"lawn"
],
"210": [
"n210",
"lecture_room"
],
"211": [
"n211",
"legislative_chamber"
],
"212": [
"n212",
"library/indoor"
],
"213": [
"n213",
"library/outdoor"
],
"214": [
"n214",
"lighthouse"
],
"215": [
"n215",
"living_room"
],
"216": [
"n216",
"loading_dock"
],
"217": [
"n217",
"lobby"
],
"218": [
"n218",
"lock_chamber"
],
"219": [
"n219",
"locker_room"
],
"220": [
"n220",
"mansion"
],
"221": [
"n221",
"manufactured_home"
],
"222": [
"n222",
"market/indoor"
],
"223": [
"n223",
"market/outdoor"
],
"224": [
"n224",
"marsh"
],
"225": [
"n225",
"martial_arts_gym"
],
"226": [
"n226",
"mausoleum"
],
"227": [
"n227",
"medina"
],
"228": [
"n228",
"mezzanine"
],
"229": [
"n229",
"moat/water"
],
"230": [
"n230",
"mosque/outdoor"
],
"231": [
"n231",
"motel"
],
"232": [
"n232",
"mountain"
],
"233": [
"n233",
"mountain_path"
],
"234": [
"n234",
"mountain_snowy"
],
"235": [
"n235",
"movie_theater/indoor"
],
"236": [
"n236",
"museum/indoor"
],
"237": [
"n237",
"museum/outdoor"
],
"238": [
"n238",
"music_studio"
],
"239": [
"n239",
"natural_history_museum"
],
"240": [
"n240",
"nursery"
],
"241": [
"n241",
"nursing_home"
],
"242": [
"n242",
"oast_house"
],
"243": [
"n243",
"ocean"
],
"244": [
"n244",
"office"
],
"245": [
"n245",
"office_building"
],
"246": [
"n246",
"office_cubicles"
],
"247": [
"n247",
"oilrig"
],
"248": [
"n248",
"operating_room"
],
"249": [
"n249",
"orchard"
],
"250": [
"n250",
"orchestra_pit"
],
"251": [
"n251",
"pagoda"
],
"252": [
"n252",
"palace"
],
"253": [
"n253",
"pantry"
],
"254": [
"n254",
"park"
],
"255": [
"n255",
"parking_garage/indoor"
],
"256": [
"n256",
"parking_garage/outdoor"
],
"257": [
"n257",
"parking_lot"
],
"258": [
"n258",
"pasture"
],
"259": [
"n259",
"patio"
],
"260": [
"n260",
"pavilion"
],
"261": [
"n261",
"pet_shop"
],
"262": [
"n262",
"pharmacy"
],
"263": [
"n263",
"phone_booth"
],
"264": [
"n264",
"physics_laboratory"
],
"265": [
"n265",
"picnic_area"
],
"266": [
"n266",
"pier"
],
"267": [
"n267",
"pizzeria"
],
"268": [
"n268",
"playground"
],
"269": [
"n269",
"playroom"
],
"270": [
"n270",
"plaza"
],
"271": [
"n271",
"pond"
],
"272": [
"n272",
"porch"
],
"273": [
"n273",
"promenade"
],
"274": [
"n274",
"pub/indoor"
],
"275": [
"n275",
"racecourse"
],
"276": [
"n276",
"raceway"
],
"277": [
"n277",
"raft"
],
"278": [
"n278",
"railroad_track"
],
"279": [
"n279",
"rainforest"
],
"280": [
"n280",
"reception"
],
"281": [
"n281",
"recreation_room"
],
"282": [
"n282",
"repair_shop"
],
"283": [
"n283",
"residential_neighborhood"
],
"284": [
"n284",
"restaurant"
],
"285": [
"n285",
"restaurant_kitchen"
],
"286": [
"n286",
"restaurant_patio"
],
"287": [
"n287",
"rice_paddy"
],
"288": [
"n288",
"river"
],
"289": [
"n289",
"rock_arch"
],
"290": [
"n290",
"roof_garden"
],
"291": [
"n291",
"rope_bridge"
],
"292": [
"n292",
"ruin"
],
"293": [
"n293",
"runway"
],
"294": [
"n294",
"sandbox"
],
"295": [
"n295",
"sauna"
],
"296": [
"n296",
"schoolhouse"
],
"297": [
"n297",
"science_museum"
],
"298": [
"n298",
"server_room"
],
"299": [
"n299",
"shed"
],
"300": [
"n300",
"shoe_shop"
],
"301": [
"n301",
"shopfront"
],
"302": [
"n302",
"shopping_mall/indoor"
],
"303": [
"n303",
"shower"
],
"304": [
"n304",
"ski_resort"
],
"305": [
"n305",
"ski_slope"
],
"306": [
"n306",
"sky"
],
"307": [
"n307",
"skyscraper"
],
"308": [
"n308",
"slum"
],
"309": [
"n309",
"snowfield"
],
"310": [
"n310",
"soccer_field"
],
"311": [
"n311",
"stable"
],
"312": [
"n312",
"stadium/baseball"
],
"313": [
"n313",
"stadium/football"
],
"314": [
"n314",
"stadium/soccer"
],
"315": [
"n315",
"stage/indoor"
],
"316": [
"n316",
"stage/outdoor"
],
"317": [
"n317",
"staircase"
],
"318": [
"n318",
"storage_room"
],
"319": [
"n319",
"street"
],
"320": [
"n320",
"subway_station/platform"
],
"321": [
"n321",
"supermarket"
],
"322": [
"n322",
"sushi_bar"
],
"323": [
"n323",
"swamp"
],
"324": [
"n324",
"swimming_hole"
],
"325": [
"n325",
"swimming_pool/indoor"
],
"326": [
"n326",
"swimming_pool/outdoor"
],
"327": [
"n327",
"synagogue/outdoor"
],
"328": [
"n328",
"television_room"
],
"329": [
"n329",
"television_studio"
],
"330": [
"n330",
"temple/asia"
],
"331": [
"n331",
"throne_room"
],
"332": [
"n332",
"ticket_booth"
],
"333": [
"n333",
"topiary_garden"
],
"334": [
"n334",
"tower"
],
"335": [
"n335",
"toyshop"
],
"336": [
"n336",
"train_interior"
],
"337": [
"n337",
"train_station/platform"
],
"338": [
"n338",
"tree_farm"
],
"339": [
"n339",
"tree_house"
],
"340": [
"n340",
"trench"
],
"341": [
"n341",
"tundra"
],
"342": [
"n342",
"underwater/ocean_deep"
],
"343": [
"n343",
"utility_room"
],
"344": [
"n344",
"valley"
],
"345": [
"n345",
"vegetable_garden"
],
"346": [
"n346",
"veterinarians_office"
],
"347": [
"n347",
"viaduct"
],
"348": [
"n348",
"village"
],
"349": [
"n349",
"vineyard"
],
"350": [
"n350",
"volcano"
],
"351": [
"n351",
"volleyball_court/outdoor"
],
"352": [
"n352",
"waiting_room"
],
"353": [
"n353",
"water_park"
],
"354": [
"n354",
"water_tower"
],
"355": [
"n355",
"waterfall"
],
"356": [
"n356",
"watering_hole"
],
"357": [
"n357",
"wave"
],
"358": [
"n358",
"wet_bar"
],
"359": [
"n359",
"wheat_field"
],
"360": [
"n360",
"wind_farm"
],
"361": [
"n361",
"windmill"
],
"362": [
"n362",
"yard"
],
"363": [
"n363",
"youth_hostel"
],
"364": [
"n364",
"zen_garden"
]
}
================================================
FILE: places_utils.py
================================================
from keras import backend as K
def preprocess_input(x, dim_ordering='default'):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
assert dim_ordering in {'tf', 'th'}
if dim_ordering == 'th':
x[:, 0, :, :] -= 104.006
x[:, 1, :, :] -= 116.669
x[:, 2, :, :] -= 122.679
# 'RGB'->'BGR'
x = x[:, ::-1, :, :]
else:
x[:, :, :, 0] -= 104.006
x[:, :, :, 1] -= 116.669
x[:, :, :, 2] -= 122.679
# 'RGB'->'BGR'
x = x[:, :, :, ::-1]
return x
================================================
FILE: vgg16_hybrid_places_1365.py
================================================
# -*- coding: utf-8 -*-
'''VGG-ImageNetPlaces365-hybrid model for Keras
1000 classes from the ImageNet and the 365 classes from
the Places365-Standard were merged to train a VGG16-based model (Hybrid1365-VGG)
# Reference:
- [Places: A 10 million Image Database for Scene Recognition](http://places2.csail.mit.edu/PAMI_places.pdf)
'''
from __future__ import division, print_function
import os
import warnings
from keras import backend as K
from keras.layers import Input
from keras.layers.core import Activation, Dense, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
from keras.layers import Conv2D
from keras.regularizers import l2
from keras.layers.core import Dropout
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras.utils import layer_utils
WEIGHTS_PATH = 'https://github.com/GKalliatakis/Keras-VGG16-places365/releases/download/v1.0/vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/GKalliatakis/Keras-VGG16-places365/releases/download/v1.0/vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels_notop.h5'
def VGG16_Hybrid_1365(include_top=True, weights='places',
input_tensor=None, input_shape=None,
pooling=None,
classes=1365):
"""Instantiates the VGG-ImageNetPlaces365-hybrid architecture.
Optionally loads weights pre-trained
on Places. Note that when using TensorFlow,
for best performance you should set
`image_data_format="channels_last"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'places' (pre-training on Places),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 244)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`, or invalid input shape
"""
if not (weights in {'places', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `places` '
'(pre-training on Places), '
'or the path to the weights file to be loaded.')
if weights == 'places' and include_top and classes != 1365:
raise ValueError('If using `weights` as places with `include_top`'
' as true, `classes` should be 1365')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=48,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block1_conv1')(img_input)
x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block1_pool", padding='valid')(x)
# Block 2
x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block2_conv1')(x)
x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block2_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block2_pool", padding='valid')(x)
# Block 3
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv1')(x)
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv2')(x)
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block3_pool", padding='valid')(x)
# Block 4
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv1')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv2')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block4_pool", padding='valid')(x)
# Block 5
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv1')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv2')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block5_pool", padding='valid')(x)
if include_top:
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.5, name='drop_fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5, name='drop_fc2')(x)
x = Dense(1365, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='vgg16-hybrid1365')
# load weights
if weights == 'places':
if include_top:
weights_path = get_file('vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first':
if include_top:
maxpool = model.get_layer(name='block5_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
elif weights is not None:
model.load_weights(weights)
return model
if __name__ == '__main__':
import urllib2
import numpy as np
from PIL import Image
from cv2 import resize
TEST_IMAGE_URL = 'http://places2.csail.mit.edu/imgs/demo/6.jpg'
image = Image.open(urllib2.urlopen(TEST_IMAGE_URL))
image = np.array(image, dtype=np.uint8)
image = resize(image, (224, 224))
image = np.expand_dims(image, 0)
model = VGG16_Hybrid_1365(weights='places')
predictions_to_return = 5
preds = model.predict(image)[0]
top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
# load the class label
file_name = 'categories_hybrid1365.txt'
if not os.access(file_name, os.W_OK):
synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_hybrid1365.txt'
os.system('wget ' + synset_url)
classes = list()
counter = 0
with open(file_name) as class_file:
for line in class_file:
if counter <=999:
tmp = line[9:]
if 0 <= counter <= 9:
tmp = tmp[:-2]
elif 10 <= counter <= 99:
tmp = tmp[:-3]
elif 100 <= counter <= 999:
tmp = tmp[:-4]
classes.append(tmp)
else:
classes.append(line.strip().split(' ')[0][3:])
counter +=1
classes = tuple(classes)
print('--PREDICTED SCENE CATEGORIES:')
# output the prediction
for i in range(0, 5):
print(classes[top_preds[i]])
# --PREDICTED SCENE CATEGORIES:
# restaurant, eating house, eating place, eatery
# folding chair
# patio, terrace
# food_court
# cafeteria
================================================
FILE: vgg16_places_365.py
================================================
# -*- coding: utf-8 -*-
'''VGG16-places365 model for Keras
# Reference:
- [Places: A 10 million Image Database for Scene Recognition](http://places2.csail.mit.edu/PAMI_places.pdf)
'''
from __future__ import division, print_function
import os
import warnings
import numpy as np
from keras import backend as K
from keras.layers import Input
from keras.layers.core import Activation, Dense, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
from keras.layers import Conv2D
from keras.regularizers import l2
from keras.layers.core import Dropout
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras.utils import layer_utils
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
WEIGHTS_PATH = 'https://github.com/GKalliatakis/Keras-VGG16-places365/releases/download/v1.0/vgg16-places365_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/GKalliatakis/Keras-VGG16-places365/releases/download/v1.0/vgg16-places365_weights_tf_dim_ordering_tf_kernels_notop.h5'
def VGG16_Places365(include_top=True, weights='places',
input_tensor=None, input_shape=None,
pooling=None,
classes=365):
"""Instantiates the VGG16-places365 architecture.
Optionally loads weights pre-trained
on Places. Note that when using TensorFlow,
for best performance you should set
`image_data_format="channels_last"` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'places' (pre-training on Places),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 244)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`, or invalid input shape
"""
if not (weights in {'places', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `places` '
'(pre-training on Places), '
'or the path to the weights file to be loaded.')
if weights == 'places' and include_top and classes != 365:
raise ValueError('If using `weights` as places with `include_top`'
' as true, `classes` should be 365')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=48,
data_format=K.image_data_format(),
require_flatten =include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block1_conv1')(img_input)
x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block1_pool", padding='valid')(x)
# Block 2
x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block2_conv1')(x)
x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block2_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block2_pool", padding='valid')(x)
# Block 3
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv1')(x)
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv2')(x)
x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block3_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block3_pool", padding='valid')(x)
# Block 4
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv1')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv2')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block4_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block4_pool", padding='valid')(x)
# Block 5
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv1')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv2')(x)
x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
kernel_regularizer=l2(0.0002),
activation='relu', name='block5_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block5_pool", padding='valid')(x)
if include_top:
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.5, name='drop_fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5, name='drop_fc2')(x)
x = Dense(365, activation='softmax', name="predictions")(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='vgg16-places365')
# load weights
if weights == 'places':
if include_top:
weights_path = get_file('vgg16-places365_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('vgg16-places365_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first':
if include_top:
maxpool = model.get_layer(name='block5_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
elif weights is not None:
model.load_weights(weights)
return model
if __name__ == '__main__':
import urllib2
import numpy as np
from PIL import Image
from cv2 import resize
TEST_IMAGE_URL = 'http://places2.csail.mit.edu/imgs/demo/6.jpg'
image = Image.open(urllib2.urlopen(TEST_IMAGE_URL))
image = np.array(image, dtype=np.uint8)
image = resize(image, (224, 224))
image = np.expand_dims(image, 0)
model = VGG16_Places365(weights='places')
predictions_to_return = 5
preds = model.predict(image)[0]
top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
# load the class label
file_name = 'categories_places365.txt'
if not os.access(file_name, os.W_OK):
synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_places365.txt'
os.system('wget ' + synset_url)
classes = list()
with open(file_name) as class_file:
for line in class_file:
classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
print('--PREDICTED SCENE CATEGORIES:')
# output the prediction
for i in range(0, 5):
print(classes[top_preds[i]])
# --PREDICTED SCENE CATEGORIES:
# cafeteria
# food_court
# restaurant_patio
# banquet_hall
# restaurant