├── img └── framework.png ├── data ├── Food-50 │ └── selected_50_classes.pkl ├── CUB-100 │ └── selected_100_classes.pkl ├── ImageNet │ └── imagenet_class_clean.npy ├── Oxford-Pet-18 │ └── selected_18_classes.pkl ├── Stanford-Cars-98 │ └── selected_98_classes.pkl ├── ImageNet10 │ ├── class_list.txt │ └── ImageNet-10-classlist.csv └── ImageNet20 │ ├── class_list.txt │ └── ImageNet-20-classlist.csv ├── utils ├── __init__.py ├── plot_util.py ├── args_pool.py ├── file_ops.py ├── common.py ├── imagenet_templates.py └── language_models.py ├── .gitignore ├── dataloaders ├── __init__.py └── bird200.py ├── requirements.txt ├── envisioned_classes ├── near_1 │ ├── ImageNet10_gpt-3.5-turbo-16k_0.json │ ├── ImageNet10_gpt-3.5-turbo-16k_1.json │ ├── ImageNet10_gpt-3.5-turbo-16k_2.json │ ├── ImageNet20_gpt-3.5-turbo-16k_0.json │ ├── ImageNet20_gpt-3.5-turbo-16k_1.json │ └── ImageNet20_gpt-3.5-turbo-16k_2.json ├── near_3 │ ├── cifar10_gpt-3.5-turbo-16k_0.json │ ├── cifar10_gpt-3.5-turbo-16k_1.json │ ├── cifar10_gpt-3.5-turbo-16k_2.json │ ├── ImageNet10_claude2_0.json │ ├── ImageNet10_claude2_2.json │ ├── ImageNet10_llama-2_1.json │ ├── ImageNet10_gpt-3.5-turbo-16k_0.json │ ├── ImageNet10_gpt-3.5-turbo-16k_1.json │ ├── ImageNet10_gpt-3.5-turbo-16k_2.json │ ├── ImageNet10_claude2_1.json │ ├── ImageNet10_llama-2_2.json │ ├── ImageNet10_llama-2_0.json │ ├── ImageNet20_gpt-3.5-turbo-16k_2.json │ ├── ImageNet20_gpt-3.5-turbo-16k_0.json │ ├── ImageNet20_gpt-3.5-turbo-16k_1.json │ ├── cifar100_gpt-3.5-turbo-16k_0.json │ ├── cifar100_gpt-3.5-turbo-16k_2.json │ └── cifar100_gpt-3.5-turbo-16k_1.json ├── near_dissimilar_3 │ ├── ImageNet10_gpt-3.5-turbo-16k_0.json │ ├── ImageNet10_gpt-3.5-turbo-16k_2.json │ └── ImageNet10_gpt-3.5-turbo-16k_1.json ├── near_irrelevant_3 │ ├── ImageNet10_gpt-3.5-turbo-16k_3.json │ ├── ImageNet10_gpt-3.5-turbo-16k_4.json │ └── ImageNet10_gpt-3.5-turbo-16k_5.json ├── far_100 │ ├── food101_gpt-3.5-turbo-16k_2.json │ ├── food101_gpt-3.5-turbo-16k_1.json │ ├── car196_gpt-3.5-turbo-16k_0.json │ ├── car196_gpt-3.5-turbo-16k_1.json │ ├── food101_gpt-3.5-turbo-16k_0.json │ ├── ImageNet_gpt-3.5-turbo-16k_1.json │ ├── pet37_gpt-3.5-turbo-16k_2.json │ ├── ImageNet_gpt-3.5-turbo-16k_0.json │ ├── car196_gpt-3.5-turbo-16k_2.json │ ├── bird200_gpt-3.5-turbo-16k_2.json │ ├── pet37_gpt-3.5-turbo-16k_0.json │ ├── pet37_gpt-3.5-turbo-16k_1.json │ ├── ImageNet_gpt-3.5-turbo-16k_2.json │ ├── bird200_gpt-3.5-turbo-16k_0.json │ └── bird200_gpt-3.5-turbo-16k_1.json ├── fine_grained_100 │ ├── food50_ID_gpt-3.5-turbo-16k_2.json │ ├── pet18_ID_gpt-3.5-turbo-16k_0.json │ ├── pet18_ID_gpt-3.5-turbo-16k_1.json │ ├── pet18_ID_gpt-3.5-turbo-16k_2.json │ ├── cub100_ID_gpt-3.5-turbo-16k_0.json │ ├── cub100_ID_gpt-3.5-turbo-16k_1.json │ ├── cub100_ID_gpt-3.5-turbo-16k_2.json │ ├── food50_ID_gpt-3.5-turbo-16k_0.json │ ├── food50_ID_gpt-3.5-turbo-16k_1.json │ ├── car98_ID_gpt-3.5-turbo-16k_2.json │ ├── car98_ID_gpt-3.5-turbo-16k_0.json │ └── car98_ID_gpt-3.5-turbo-16k_1.json ├── near_10 │ ├── ImageNet10_gpt-3.5-turbo-16k_0.json │ ├── ImageNet10_gpt-3.5-turbo-16k_1.json │ ├── ImageNet10_gpt-3.5-turbo-16k_2.json │ ├── ImageNet20_gpt-3.5-turbo-16k_2.json │ ├── ImageNet20_gpt-3.5-turbo-16k_0.json │ └── ImageNet20_gpt-3.5-turbo-16k_1.json ├── far_300 │ ├── car196_gpt-3.5-turbo-16k_1.json │ ├── car196_gpt-3.5-turbo-16k_0.json │ ├── ImageNet_gpt-3.5-turbo-16k_1.json │ ├── bird200_gpt-3.5-turbo-16k_0.json │ ├── car196_gpt-3.5-turbo-16k_2.json │ ├── food101_gpt-3.5-turbo-16k_1.json │ ├── pet37_gpt-3.5-turbo-16k_0.json │ ├── pet37_gpt-3.5-turbo-16k_2.json │ ├── pet37_gpt-3.5-turbo-16k_1.json │ ├── food101_gpt-3.5-turbo-16k_0.json │ └── ImageNet_gpt-3.5-turbo-16k_2.json ├── far_500 │ └── pet37_gpt-3.5-turbo-16k_1.json └── fine_grained_300 │ ├── pet18_ID_gpt-3.5-turbo-16k_1.json │ ├── pet18_ID_gpt-3.5-turbo-16k_0.json │ ├── pet18_ID_gpt-3.5-turbo-16k_2.json │ ├── cub100_ID_gpt-3.5-turbo-16k_0.json │ ├── cub100_ID_gpt-3.5-turbo-16k_2.json │ └── cub100_ID_gpt-3.5-turbo-16k_1.json ├── eval.sh └── eval_ood_detection.py /img/framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/img/framework.png -------------------------------------------------------------------------------- /data/Food-50/selected_50_classes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/data/Food-50/selected_50_classes.pkl -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from .common import * 3 | from .train_eval_util import * 4 | -------------------------------------------------------------------------------- /data/CUB-100/selected_100_classes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/data/CUB-100/selected_100_classes.pkl -------------------------------------------------------------------------------- /data/ImageNet/imagenet_class_clean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/data/ImageNet/imagenet_class_clean.npy -------------------------------------------------------------------------------- /data/Oxford-Pet-18/selected_18_classes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/data/Oxford-Pet-18/selected_18_classes.pkl -------------------------------------------------------------------------------- /data/Stanford-Cars-98/selected_98_classes.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aboriginer/EOE/HEAD/data/Stanford-Cars-98/selected_98_classes.pkl -------------------------------------------------------------------------------- /data/ImageNet10/class_list.txt: -------------------------------------------------------------------------------- 1 | n04552348 2 | n04285008 3 | n01530575 4 | n02123597 5 | n02422699 6 | n02107574 7 | n01641577 8 | n03417042 9 | n02389026 10 | n03095699 11 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # vscode debug 2 | .vscode/ 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | 7 | .env 8 | # ignore data and output directory 9 | datasets/ 10 | results*/ 11 | plot/ -------------------------------------------------------------------------------- /dataloaders/__init__.py: -------------------------------------------------------------------------------- 1 | from .pet37 import OxfordIIITPet, OxfordIIITPet_18 2 | from .car196 import StanfordCars, StanfordCars98 3 | from .food101 import Food101, Food101_50 4 | from .bird200 import Cub2011, Cub100 5 | -------------------------------------------------------------------------------- /data/ImageNet10/ImageNet-10-classlist.csv: -------------------------------------------------------------------------------- 1 | n04552348,warplane 2 | n04285008,sportscar 3 | n01530575,bramblingbird 4 | n02123597,Siamesecat 5 | n02422699,antelope 6 | n02107574,swissmountaindog 7 | n01641577,bullfrog 8 | n03417042,garbagetruck 9 | n02389026,horse 10 | n03095699,containership 11 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==1.4.0 2 | torch==1.13.1 3 | torchvision==0.14.1 4 | openai==0.28.0 5 | numpy==1.23.5 6 | scipy==1.10.1 7 | seaborn==0.12.2 8 | transformers==4.32.1 9 | timm==0.9.12 10 | scikit-learn==1.2.2 11 | fastapi-poe==0.0.44 12 | fastapi==0.110.0 13 | python-dotenv==1.0.1 14 | 15 | -------------------------------------------------------------------------------- /data/ImageNet20/class_list.txt: -------------------------------------------------------------------------------- 1 | n04147183 2 | n02951358 3 | n02782093 4 | n04389033 5 | n03773504 6 | n02917067 7 | n02317335 8 | n01632458 9 | n01630670 10 | n01631663 11 | n02391049 12 | n01693334 13 | n01697457 14 | n02120079 15 | n02114367 16 | n02132136 17 | n03785016 18 | n04310018 19 | n04266014 20 | n04252077 21 | -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet10_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch"], "bull frog": ["tree frog"], "swiss mountain dog": ["alpaca"], "Siamese cat": ["Bengal tiger"], "horse": ["zebra"], "antelope": ["gazelle"], "container ship": ["lighthouse"], "garbage truck": ["cement mixer"], "sports car": ["speedboat"], "warplane": ["rocket"]} -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet10_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch"], "bull frog": ["tree frog"], "swiss mountain dog": ["alpaca"], "Siamese cat": ["snow leopard"], "horse": ["zebra"], "antelope": ["gazelle"], "container ship": ["lighthouse"], "garbage truck": ["cement mixer"], "sports car": ["speedboat"], "warplane": ["rocket"]} -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet10_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch"], "bull frog": ["tree frog"], "swiss mountain dog": ["alpaca"], "Siamese cat": ["snow leopard"], "horse": ["zebra"], "antelope": ["gazelle"], "container ship": ["cargo plane"], "garbage truck": ["cement mixer"], "sports car": ["speedboat"], "warplane": ["rocket"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar10_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"airplane": ["seagull", "hot air balloon", "wind turbine"], "automobile": ["motorcycle", "skateboard", "bicycle"], "bird": ["airplane", "flower", "fish"], "cat": ["tiger", "lion", "cheetah"], "deer": ["antelope", "kangaroo", "moose"], "dog": ["cat", "horse", "rabbit"], "frog": ["lizard", "tree", "mushroom"], "horse": ["zebra", "giraffe", "deer"], "ship": ["airplane", "lighthouse", "hot air balloon"], "truck": ["bulldozer", "sailboat", "airplane"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar10_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"airplane": ["seagull", "hot air balloon", "rocket"], "automobile": ["motorcycle", "skateboard", "bicycle"], "bird": ["airplane", "flower", "fish"], "cat": ["tiger", "lion", "cheetah"], "deer": ["antelope", "kangaroo", "moose"], "dog": ["cat", "horse", "rabbit"], "frog": ["lizard", "tree", "mushroom"], "horse": ["zebra", "giraffe", "deer"], "ship": ["airplane", "lighthouse", "hot air balloon"], "truck": ["bulldozer", "sailboat", "hot air balloon"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar10_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"airplane": ["seagull", "hot air balloon", "wind turbine"], "automobile": ["skateboard", "shopping cart", "motorcycle"], "bird": ["airplane", "flower", "fish"], "cat": ["tiger", "leopard", "lynx"], "deer": ["antelope", "kangaroo", "moose"], "dog": ["cat", "horse", "rabbit"], "frog": ["lizard", "tree", "mushroom"], "horse": ["zebra", "giraffe", "deer"], "ship": ["airplane", "lighthouse", "hot air balloon"], "truck": ["bulldozer", "sailboat", "airplane"]} -------------------------------------------------------------------------------- /data/ImageNet20/ImageNet-20-classlist.csv: -------------------------------------------------------------------------------- 1 | n04147183,sailboat 2 | n02951358,canoe 3 | n02782093,balloon 4 | n04389033,tank 5 | n03773504,missile 6 | n02917067,bullettrain 7 | n02317335,starfish 8 | n01632458,spottedsalamander 9 | n01630670,commonnewt 10 | n01631663,eft 11 | n02391049,zebra 12 | n01693334,greenlizard 13 | n01697457,Africancrocodile 14 | n02120079,Arcticfox 15 | n02114367,timberwolf 16 | n02132136,brownbear 17 | n03785016,moped 18 | n04310018,steamlocomotive 19 | n04266014,spaceshuttle 20 | n04252077,snowmobile 21 | -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_claude2_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Butterfly", "Flower", "Kite"], "bull frog": ["Baseball", "Tomato", "Red Bell Pepper"], "swiss mountain dog": ["Sheep", "Mop", "Dust Bunny"], "Siamese cat": ["Raccoon", "Skunk", "Panda"], "horse": ["Giraffe", "Guitar", "Baseball bat"], "antelope": ["Rocking Horse", "Guitar", "Baseball Bat"], "container ship": ["Skyscraper", "Train", "Caterpillar"], "garbage truck": ["Caterpillar", "Train", "Shipping Container"], "sports car": ["High Heel Shoe", "Rocket", "Bullet Train"], "warplane": ["Dragonfly", "Paper airplane", "Kite"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_claude2_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Strawberry", "Ladybug", "Cherry blossoms"], "bull frog": ["Green bell pepper", "Golf ball", "Jade stone"], "swiss mountain dog": ["Sheep", "Polar bear", "Cotton ball"], "Siamese cat": ["Raccoon", "Skunk", "Panda"], "horse": ["Giraffe", "Violin", "Banana"], "antelope": ["French horn", "Pretzel", "Teapot"], "container ship": ["Skyscraper", "Train", "Caterpillar"], "garbage truck": ["Caterpillar", "Train", "Monster truck"], "sports car": ["High heel shoe", "Rocket", "Bullet Train"], "warplane": ["Eagle", "Shark", "Bullet train"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_llama-2_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Butterflies", "Flowers", "Geometric shapes"], "bull frog": ["Underwater plants", "Landscapes", "Abstract Art"], "swiss mountain dog": ["Landscapes", "Fruits", "abstract art"], "Siamese cat": ["Floral patterns", "Geometric shapes", "Abstract art"], "horse": ["Waves", "Crystals", "Urban landscapes"], "antelope": ["Clouds", "Fractals", "Geometric shapes"], "container ship": ["Mountains", "Neurons", "galaxies"], "garbage truck": ["Clouds", "Coral", "Mountains"], "sports car": ["Jewelry", "Spacecraft", "Dance"], "warplane": ["Dragonfly", "Surfboard", "Fish"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "peacock", "flamingo"], "bull frog": ["tree frog", "poison dart frog", "salamander"], "swiss mountain dog": ["brown bear", "alpine landscape", "hiking boots"], "Siamese cat": ["blue jay", "red fox", "yellow butterfly"], "horse": ["zebra", "giraffe", "deer"], "antelope": ["gazelle", "wildebeest", "kangaroo"], "container ship": ["lighthouse", "hot air balloon", "windmill"], "garbage truck": ["cement mixer", "fire engine", "tractor"], "sports car": ["racehorse", "fighter jet", "speedboat"], "warplane": ["rocket", "submarine", "race car"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "peacock", "flamingo"], "bull frog": ["tree frog", "poison dart frog", "salamander"], "swiss mountain dog": ["brown bear", "alpine landscape", "hiking boots"], "Siamese cat": ["blue jay", "red fox", "yellow butterfly"], "horse": ["zebra", "giraffe", "deer"], "antelope": ["gazelle", "wildebeest", "kangaroo"], "container ship": ["lighthouse", "hot air balloon", "windmill"], "garbage truck": ["cement mixer", "fire engine", "tractor"], "sports car": ["racehorse", "speedboat", "fighter jet"], "warplane": ["rocket", "submarine", "race car"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "peacock", "flamingo"], "bull frog": ["tree frog", "poison dart frog", "salamander"], "swiss mountain dog": ["brown bear", "alpine landscape", "hiking boots"], "Siamese cat": ["blue jay", "red fox", "yellow butterfly"], "horse": ["zebra", "giraffe", "deer"], "antelope": ["gazelle", "wildebeest", "kangaroo"], "container ship": ["lighthouse", "hot air balloon", "windmill"], "garbage truck": ["cement mixer", "fire engine", "tractor"], "sports car": ["racehorse", "fighter jet", "speedboat"], "warplane": ["rocket", "submarine", "race car"]} -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet20_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog"], "eft": ["salamander"], "spotted salamander": ["leopard"], "green lizard": ["tree frog"], "African crocodile": ["alligator"], "timber wolf": ["gray squirrel"], "Arctic fox": ["snowshoe hare"], "brown bear": ["grizzly bear"], "starfish": ["sea urchin"], "zebra": ["giraffe"], "balloon": ["parachute"], "bullet train": ["roller coaster"], "canoe": ["kayak"], "missile": ["rocket"], "moped": ["scooter"], "sailboat": ["hot air balloon"], "snowmobile": ["motorcycle"], "space shuttle": ["airplane"], "steam locomotive": ["vintage car"], "tank": ["submarine"]} -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet20_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog"], "eft": ["salamander"], "spotted salamander": ["leopard"], "green lizard": ["tree frog"], "African crocodile": ["alligator"], "timber wolf": ["gray squirrel"], "Arctic fox": ["snowshoe hare"], "brown bear": ["grizzly bear"], "starfish": ["sea urchin"], "zebra": ["giraffe"], "balloon": ["parachute"], "bullet train": ["roller coaster"], "canoe": ["kayak"], "missile": ["rocket"], "moped": ["skateboard"], "sailboat": ["hot air balloon"], "snowmobile": ["motorcycle"], "space shuttle": ["airplane"], "steam locomotive": ["vintage car"], "tank": ["submarine"]} -------------------------------------------------------------------------------- /envisioned_classes/near_1/ImageNet20_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog"], "eft": ["salamander"], "spotted salamander": ["leopard"], "green lizard": ["tree frog"], "African crocodile": ["alligator"], "timber wolf": ["gray squirrel"], "Arctic fox": ["snow leopard"], "brown bear": ["grizzly bear"], "starfish": ["sea urchin"], "zebra": ["giraffe"], "balloon": ["parachute"], "bullet train": ["roller coaster"], "canoe": ["kayak"], "missile": ["rocket"], "moped": ["skateboard"], "sailboat": ["hot air balloon"], "snowmobile": ["motorcycle"], "space shuttle": ["airplane"], "steam locomotive": ["vintage car"], "tank": ["submarine"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_claude2_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Strawberry", "Red bell pepper", "Cherry blossom"], "bull frog": ["Green bell pepper", "Kiwi", "Green highlighter"], "swiss mountain dog": ["Sheep", "Polar bear", "Cotton ball"], "Siamese cat": ["Raccoon", "Badger", "Skunk"], "horse": ["Giraffe", "Rocking chair", "Banana"], "antelope": ["Rocking horse", "Electric guitar", "Crescent moon"], "container ship": ["Apartment building", "Train", "Filing cabinet"], "garbage truck": ["Dumpster", "Shipping container", "Refrigerator"], "sports car": ["Motorcycle", "High heel shoe", "Shark"], "warplane": ["Eagle", "Paper airplane", "Stingray"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_llama-2_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Butterflies", "Dragonflies", "Peacock"], "bull frog": ["Sunflower", "Hibiscus", "Lion's Mane Jellyfish"], "swiss mountain dog": ["Snow-Capped Mountains", "Waterfalls", "Galapagos Tortoise"], "Siamese cat": ["Japanese Maple Tree", "Peacock Feathers", "Lotus Flower"], "horse": ["Sunsets", "Lavender Fields", "Waves"], "antelope": ["Clouds", "Waterfalls", "Peacock Feathers"], "container ship": ["Mountains", "Flowers", "Clouds"], "garbage truck": ["Balloons", "Waves", "Feathers"], "sports car": ["Dragonflies", "Lightning", "Swans"], "warplane": ["Surfboards", "Butterflies", "Flowers"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet10_llama-2_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["Butterflies", "Dragonflies", "Peacock Spiders"], "bull frog": ["Seashells", "Geometric shapes", "Architectural details"], "swiss mountain dog": ["Whispers", "Clouds", "Waves"], "Siamese cat": ["Japanese Gardens", "Geisha Robes", "Antique Furniture"], "horse": ["Waves", "Lights", "Fabrics"], "antelope": ["Flowers", "Waterfalls", "Clouds"], "container ship": ["Architecture", "Jewelry", "Landscapes"], "garbage truck": ["Spacecraft", "Fashion runway", "Ocean waves"], "sports car": ["Fine jewelry", "High-end kitchen appliances", "Luxury yachts"], "warplane": ["Dragonfly", "Supercar", "Spacecraft"]} -------------------------------------------------------------------------------- /envisioned_classes/near_dissimilar_3/ImageNet10_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater coral reefs", "mountain landscapes", "abstract art"], "bull frog": ["mountain landscapes", "abstract art", "vintage cars"], "swiss mountain dog": ["coral reefs", "city skylines", "outer space"], "Siamese cat": ["mountain landscapes", "abstract art", "underwater creatures"], "horse": ["underwater creatures", "city skylines", "mountain landscapes"], "antelope": ["underwater coral reefs", "city skylines", "outer space galaxies"], "container ship": ["rainforest", "mountain range", "art museum"], "garbage truck": ["underwater creatures", "mountain landscapes", "musical instruments"], "sports car": ["underwater creatures", "mountain landscapes", "abstract art"], "warplane": ["underwater creatures", "mountain landscapes", "musical instruments"]} -------------------------------------------------------------------------------- /envisioned_classes/near_dissimilar_3/ImageNet10_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater coral reefs", "mountain landscapes", "abstract art"], "bull frog": ["mountain landscapes", "abstract art", "vintage cars"], "swiss mountain dog": ["coral reefs", "city skylines", "outer space"], "Siamese cat": ["mountain landscapes", "abstract art", "underwater creatures"], "horse": ["underwater creatures", "city skylines", "mountain landscapes"], "antelope": ["underwater coral reefs", "city skylines", "outer space galaxies"], "container ship": ["rainforest", "mountain range", "art museum"], "garbage truck": ["underwater creatures", "mountain landscapes", "musical instruments"], "sports car": ["underwater creatures", "mountain landscapes", "abstract art"], "warplane": ["underwater creatures", "mountain landscapes", "musical instruments"]} -------------------------------------------------------------------------------- /envisioned_classes/near_irrelevant_3/ImageNet10_gpt-3.5-turbo-16k_3.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater creatures", "abstract art", "modern architecture"], "bull frog": ["abstract art", "fashion accessories", "space exploration"], "swiss mountain dog": ["abstract art", "space exploration", "fashion design"], "Siamese cat": ["mountain landscapes", "abstract art", "vintage cars"], "horse": ["abstract art", "space exploration", "fashion design"], "antelope": ["abstract art", "fashion accessories", "modern architecture"], "container ship": ["mountain landscapes", "fashion accessories", "abstract paintings"], "garbage truck": ["fashion accessories", "musical instruments", "space exploration"], "sports car": ["underwater creatures", "abstract art", "traditional clothing"], "warplane": ["floral arrangements", "fashion accessories", "culinary dishes"]} -------------------------------------------------------------------------------- /envisioned_classes/near_irrelevant_3/ImageNet10_gpt-3.5-turbo-16k_4.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater creatures", "abstract art", "modern architecture"], "bull frog": ["abstract art", "fashion accessories", "space exploration"], "swiss mountain dog": ["abstract art", "space exploration", "fashion design"], "Siamese cat": ["mountain landscapes", "abstract art", "vintage cars"], "horse": ["abstract art", "space exploration", "fashion design"], "antelope": ["abstract art", "fashion accessories", "modern architecture"], "container ship": ["mountain landscapes", "fashion accessories", "abstract paintings"], "garbage truck": ["fashion accessories", "musical instruments", "space exploration"], "sports car": ["underwater creatures", "abstract art", "traditional clothing"], "warplane": ["floral arrangements", "fashion accessories", "culinary dishes"]} -------------------------------------------------------------------------------- /envisioned_classes/near_irrelevant_3/ImageNet10_gpt-3.5-turbo-16k_5.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater creatures", "abstract art", "modern architecture"], "bull frog": ["abstract art", "fashion accessories", "space exploration"], "swiss mountain dog": ["abstract art", "space exploration", "fashion design"], "Siamese cat": ["mountain landscapes", "abstract art", "vintage cars"], "horse": ["abstract art", "space exploration", "fashion design"], "antelope": ["abstract art", "fashion accessories", "modern architecture"], "container ship": ["mountain landscapes", "fashion accessories", "abstract paintings"], "garbage truck": ["fashion accessories", "musical instruments", "space exploration"], "sports car": ["underwater creatures", "abstract art", "traditional clothing"], "warplane": ["floral arrangements", "fashion accessories", "culinary dishes"]} -------------------------------------------------------------------------------- /envisioned_classes/near_dissimilar_3/ImageNet10_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["underwater coral reefs", "mountain landscapes", "abstract art"], "bull frog": ["mountain landscapes", "abstract art", "vintage cars"], "swiss mountain dog": ["coral reefs", "city skylines", "outer space"], "Siamese cat": ["mountain landscapes", "abstract art", "underwater creatures"], "horse": ["underwater creatures", "city skylines", "mountain landscapes"], "antelope": ["underwater coral reefs", "city skylines", "celestial bodies (stars, planets, galaxies)"], "container ship": ["rainforest", "mountain range", "art museum"], "garbage truck": ["underwater creatures", "mountain landscapes", "musical instruments"], "sports car": ["underwater creatures", "mountain landscapes", "abstract art"], "warplane": ["underwater creatures", "mountain landscapes", "musical instruments"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/food101_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Orchid", "Watermelon", "Sunflower", "Pineapple", "Kiwi", "Mango", "Avocado", "Papaya", "Dragonfruit", "Guava", "Passionfruit", "Lychee", "Durian", "Jackfruit", "Coconut", "Pomegranate", "Fig", "Persimmon", "Starfruit", "Raspberry", "Blueberry", "Blackberry", "Cranberry", "Strawberry", "Cherry", "Plum", "Apricot", "Peach", "Pear", "Apple", "Orange", "Lemon", "Lime", "Grapefruit", "Banana", "Pinecone", "Acorn", "Maple leaf", "Oak leaf", "Palm tree", "Bamboo", "Cactus", "Sunflower", "Tulip", "Rose", "Lily", "Daisy", "Dandelion", "Lavender- Sunset", "Waterfall", "Beach", "Forest", "Mountain", "Desert", "Canyon", "Lake", "River", "Ocean", "Clouds", "Sky", "Sunrise", "Moon", "Stars", "Aurora", "Snow", "Rain", "Lightning", "Fog", "Mist", "Rainbow", "Volcano", "Glacier", "Island", "Cave", "Ruins", "Castle", "Bridge", "Statue", "Temple", "Mosque", "Pyramid", "Tower", "Lighthouse", "Windmill", "Barn", "Cottage", "Mansion", "Skyscraper", "Street", "Park", "Garden", "Farm", "Market", "Stadium", "Theater", "Museum", "Library", "Zoo"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/food101_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Orchid", "Watermelon", "Sunflower", "Pineapple", "Kiwi", "Mango", "Avocado", "Papaya", "Dragonfruit", "Guava", "Passionfruit", "Lychee", "Durian", "Jackfruit", "Coconut", "Pomegranate", "Fig", "Persimmon", "Starfruit", "Raspberry", "Blueberry", "Blackberry", "Cranberry", "Strawberry", "Cherry", "Plum", "Apricot", "Peach", "Pear", "Apple", "Orange", "Lemon", "Lime", "Grapefruit", "Banana", "Pinecone", "Acorn", "Maple leaf", "Oak leaf", "Palm tree", "Bamboo", "Cactus", "Sunflower", "Tulip", "Rose", "Lily", "Daisy", "Dandelion", "Lavender- Sunset", "Waterfall", "Beach", "Forest", "Mountain", "Desert", "Canyon", "Lake", "River", "Ocean", "Clouds", "Sky", "Sunrise", "Snowy peak", "Meadow", "Field", "Valley", "Cave", "Volcano", "Glacier", "Rainforest", "Savannah", "Tundra", "Prairie", "Wetlands", "Marsh", "Pond", "Island", "Cliff", "Rock formation", "Sand dunes", "Coral reef", "Water stream", "Waterfall", "Hot spring", "Geothermal pool", "Lagoon", "Fjord", "Archipelago", "Geyser", "Crater", "Rapids", "Rapids", "Rapids", "Rapids", "Rapids", "Rapids"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet20_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["tree frog", "chameleon", "salamander"], "eft": ["gecko", "chameleon", "salamander"], "spotted salamander": ["leopard", "ladybug", "cheetah"], "green lizard": ["tree frog", "chameleon", "iguana"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard"], "timber wolf": ["gray squirrel", "black bear", "red fox"], "Arctic fox": ["snow leopard", "polar bear", "snowy owl"], "brown bear": ["grizzly bear", "panda bear", "teddy bear"], "starfish": ["jellyfish", "sea anemone", "coral"], "zebra": ["giraffe", "panda", "cow"], "balloon": ["bubbles", "jellyfish", "lollipops"], "bullet train": ["roller coaster", "race car", "rocket"], "canoe": ["kayak", "paddleboard", "sailboat"], "missile": ["rocket ship", "fireworks", "submarine"], "moped": ["skateboard", "shopping cart", "surfboard"], "sailboat": ["hot air balloon", "windmill", "lighthouse"], "snowmobile": ["motorcycle", "speedboat", "tractor"], "space shuttle": ["airplane", "submarine", "skyscraper"], "steam locomotive": ["vintage car", "old airplane", "antique bicycle"], "tank": ["submarine", "bulldozer", "spaceship"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet20_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["tree frog", "chameleon", "salamander"], "eft": ["gecko", "newt", "salamander"], "spotted salamander": ["leopard", "ladybug", "cheetah"], "green lizard": ["tree frog", "chameleon", "iguana"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard"], "timber wolf": ["gray squirrel", "black bear", "red fox"], "Arctic fox": ["snow leopard", "polar bear", "snowy owl"], "brown bear": ["grizzly bear", "panda bear", "teddy bear"], "starfish": ["sea urchin", "sand dollar", "sea cucumber"], "zebra": ["giraffe", "panda", "cow"], "balloon": ["bubbles", "jellyfish", "lollipops"], "bullet train": ["roller coaster", "race car", "rocket"], "canoe": ["kayak", "paddleboard", "sailboat"], "missile": ["rocket ship", "fireworks", "submarine"], "moped": ["skateboard", "shopping cart", "surfboard"], "sailboat": ["hot air balloon", "windmill", "lighthouse"], "snowmobile": ["motorcycle", "speedboat", "tractor"], "space shuttle": ["airplane", "submarine", "skyscraper"], "steam locomotive": ["vintage car", "old airplane", "antique bicycle"], "tank": ["submarine", "bulldozer", "spaceship"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/ImageNet20_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["tree frog", "chameleon", "salamander"], "eft": ["gecko", "chameleon", "salamander"], "spotted salamander": ["leopard", "ladybug", "cheetah"], "green lizard": ["tree frog", "chameleon", "iguana"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard"], "timber wolf": ["gray squirrel", "black bear", "red fox"], "Arctic fox": ["snow leopard", "polar bear", "snowy owl"], "brown bear": ["grizzly bear", "panda bear", "teddy bear"], "starfish": ["jellyfish", "sea anemone", "coral"], "zebra": ["barcode", "piano keys", "referee"], "balloon": ["bubbles", "jellyfish", "lollipops"], "bullet train": ["roller coaster", "race car", "rocket"], "canoe": ["kayak", "paddleboard", "sailboat"], "missile": ["rocket ship", "fireworks", "submarine"], "moped": ["bicycle", "scooter", "skateboard"], "sailboat": ["hot air balloon", "windmill", "lighthouse"], "snowmobile": ["motorcycle", "speedboat", "tractor"], "space shuttle": ["airplane", "submarine", "hot air balloon"], "steam locomotive": ["vintage car", "old airplane", "antique bicycle"], "tank": ["submarine", "bulldozer", "spaceship"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/car196_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Hot air balloon", "Helicopter", "Jet", "Spaceship- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon basket", "Zeppelin", "Blimp", "Bobsled", "Speed skates", "Ice skates", "Water skis", "Wakeboard", "Paddleboard", "Raft", "Canoe paddle", "Kayak paddle", "Life jacket", "Diving mask", "Snorkel", "Scuba gear", "Fishing rod", "Tennis racket", "Golf club", "Baseball bat", "Soccer ball", "Basketball", "Football", "Volleyball", "Frisbee", "Bowling ball", "Pool cue", "Ping pong paddle", "Badminton racket", "Croquet mallet", "Hula hoop"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/car196_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Hot air balloon", "Helicopter", "Jet", "Spaceship- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Hang glider", "Parachute", "Hot rod", "RV trailer", "Food cart", "Ice cream cart", "Popcorn cart", "Coffee cart", "Fruit cart", "Flower cart", "Book cart", "Tool cart", "Golf cart", "Fire extinguisher", "Life jacket", "Safety cone", "Traffic light", "Stop sign", "Speed limit sign", "Road sign", "Construction sign", "Parking sign", "No entry sign", "Bicycle lane sign", "Crosswalk sign", "Pedestrian sign", "Bus stop sign", "Train station sign", "Airport sign", "Harbor sign", "Gas station sign", "Car wash sign", "Drive-thru sign", "ATM sign", "Pharmacy sign"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/food101_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Orchid", "Watermelon", "Sunflower", "Pineapple", "Kiwi", "Mango", "Avocado", "Papaya", "Dragonfruit", "Guava", "Passionfruit", "Lychee", "Durian", "Jackfruit", "Pomegranate", "Persimmon", "Fig", "Coconut", "Starfruit", "Raspberry", "Blueberry", "Blackberry", "Cranberry", "Strawberry", "Cherry", "Plum", "Apricot", "Peach", "Pear", "Apple", "Orange", "Lemon", "Lime", "Grapefruit", "Banana", "Pinecone", "Acorn", "Walnut", "Chestnut", "Almond", "Pistachio", "Cashew", "Hazelnut", "Macadamia", "Peanut", "Pecan", "Brazil nut", "Walnut", "Butternut squash- Sunflower field", "Ocean waves", "Mountain range", "Desert landscape", "Autumn leaves", "Snow-capped peaks", "City skyline", "Rainforest canopy", "Sunset beach", "Rolling hills", "Waterfall cascade", "Starry night sky", "Aurora borealis", "Cherry blossom", "Tulip garden", "Lavender field", "Bamboo forest", "Moss-covered rocks", "Sand dunes", "Cactus garden", "Wheat field", "Vineyard rows", "Mossy tree trunk", "Mossy stone wall", "Mossy forest floor", "Mossy log", "Mossy pathway", "Mossy waterfall", "Mossy cave", "Mossy bridge", "Mossy pond", "Mossy roof", "Mossy steps", "Mossy fence", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy gate", "Mossy window", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate", "Mossy gate"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/ImageNet_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Sunset", "Rainbow", "Snowflake", "Aurora borealis", "Galaxy", "Lightning", "Fireworks", "Autumn leaves", "Cherry blossom", "Bamboo forest", "Wheat field", "Tulip garden", "Lavender field", "Mossy forest", "Rocky mountains", "Sandy beach", "Coral reef", "Iceberg", "Glacier", "Canyon", "Cactus", "Palm tree", "Bamboo", "Maple tree", "Oak tree", "Pine tree", "Birch tree", "Willow tree", "Rose", "Lily", "Orchid", "Daisy", "Poppy", "Tulip", "Sunflower", "Daffodil", "Iris", "Hydrangea", "Peony", "Carnation", "Chrysanthemum", "Lotus", "Pansy", "Marigold", "Dahlia", "Gerbera- Waterfall", "Aurora Borealis", "Desert Oasis", "Mountain Range", "Sunset Skyline", "Rainbow Spectrum", "Snowy Landscape", "Starry Night", "Lightning Storm", "Firework Display", "Autumn Foliage", "Cherry Blossoms", "Bamboo Forest", "Wheat Field", "Tulip Garden", "Lavender Field", "Mossy Forest", "Rocky Cliffs", "Sandy Shoreline", "Coral Reef", "Iceberg Formation", "Glacial Valley", "Canyon Walls", "Desert Cacti", "Palm Oasis", "Bamboo Grove", "Maple Leaves", "Oak Woodland", "Pine Forest", "Birch Grove", "Willow Tree", "Rose Garden", "Lily Pond", "Orchid Collection", "Daisy Meadow", "Poppy Field", "Tulip Bouquet", "Sunflower Field", "Daffodil Garden", "Iris Patch", "Hydrangea Bush", "Peony Blossoms", "Carnation Bouquet", "Chrysanthemum Display", "Lotus Pond", "Pansy Bed", "Marigold Garden", "Dahlia Collection", "Gerbera Daisies"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/food50_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Pizza", "Tacos", "Sushi Burrito", "Pad Thai", "Pho", "Ramen", "Chicken Tikka Masala", "Butter Chicken", "Biryani", "Naan Bread", "Falafel", "Hummus", "Shawarma", "Gyro", "Tandoori Chicken", "Samosa", "Tikka Masala", "Masala Dosa", "Chole Bhature", "Vada Pav", "Pani Puri", "Pav Bhaji", "Dahi Puri", "Misal Pav", "Dhokla", "Kachori", "Jalebi", "Lassi", "Bhelpuri", "Aloo Paratha", "Chole Kulche", "Rajma Chawal", "Chole Bhature", "Pani Puri", "Pav Bhaji", "Dahi Puri", "Misal Pav", "Dhokla", "Kachori", "Jalebi", "Lassi", "Bhelpuri", "Aloo Paratha", "Chole Kulche", "Rajma Chawal", "Chole Bhature", "Pani Puri", "Pav Bhaji", "Dahi Puri", "Misal Pav", "Dhokla", "Kachori", "Jalebi", "Lassi", "Bhelpuri", "Aloo Paratha", "Chole Kulche", "Rajma Chawal- Tacos", "Pizza", "Sushi Burrito", "Pad Thai", "Pho", "Ramen", "Chicken Tikka Masala", "Butter Chicken", "Biryani", "Naan Bread", "Falafel", "Hummus", "Shawarma", "Gyro", "Tandoori Chicken", "Samosa", "Tikka Masala", "Masala Dosa", "Chole Bhature", "Vada Pav", "Pani Puri", "Pav Bhaji", "Dahi Puri", "Misal Pav", "Dhokla", "Kachori", "Jalebi", "Lassi", "Bhelpuri", "Aloo Paratha", "Chole Kulche", "Rajma Chawal", "Fish and Chips", "Lobster Thermidor", "Beef Stroganoff", "Chicken Parmesan", "Chicken Cordon Bleu", "Beef Wellington", "Lobster Thermidor", "Beef Stroganoff", "Chicken Parmesan", "Chicken Cordon Bleu", "Beef Wellington", "Lobster Thermidor", "Beef Stroganoff", "Chicken Parmesan", "Chicken Cordon Bleu", "Beef Wellington"]} -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet10_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "cardinal bird", "blue jay bird", "robin bird", "sparrow bird", "hummingbird", "parrot bird", "toucan bird", "penguin bird", "flamingo bird"], "bull frog": ["tree frog", "poison dart frog", "toad", "salamander", "lizard", "turtle", "snake", "crocodile", "alligator", "iguana"], "swiss mountain dog": ["Bernese mountain dog", "Saint Bernard", "Great Pyrenees", "Alaskan Malamute", "Siberian Husky", "Newfoundland", "Leonberger", "Tibetan Mastiff", "Caucasian Shepherd Dog", "Great Dane"], "Siamese cat": ["Bengal tiger", "Snow leopard", "Lynx", "Cheetah", "Leopard", "Jaguar", "Ocelot", "Serval", "Caracal", "Bobcat"], "horse": ["zebra", "giraffe", "cow", "deer", "camel", "elephant", "rhinoceros", "kangaroo", "donkey", "goat"], "antelope": ["gazelle", "deer", "horse", "giraffe", "zebra", "kangaroo", "llama", "camel", "goat", "sheep"], "container ship": ["cruise ship", "oil tanker", "cargo plane", "train", "semi-truck", "warehouse", "construction crane", "shipping container", "cargo truck", "cargo train"], "garbage truck": ["cement mixer", "fire truck", "tow truck", "dump truck", "delivery truck", "food truck", "ice cream truck", "moving truck", "tanker truck", "pickup truck"], "sports car": ["racehorse", "speedboat", "fighter jet", "motorcycle", "roller coaster", "rocket", "cheetah", "bullet train", "Formula 1 car", "jet ski"], "warplane": ["helicopter", "rocket", "hot air balloon", "blimp", "drone", "spaceship", "fighter jet", "bomber", "glider", "airship"]} -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet10_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "cardinal bird", "blue jay bird", "robin bird", "sparrow bird", "hummingbird", "parrot bird", "toucan bird", "penguin bird", "flamingo bird"], "bull frog": ["tree frog", "poison dart frog", "toad", "salamander", "lizard", "turtle", "snake", "crocodile", "alligator", "iguana"], "swiss mountain dog": ["Bernese mountain dog", "Saint Bernard", "Great Pyrenees", "Alaskan Malamute", "Siberian Husky", "Newfoundland", "Leonberger", "Tibetan Mastiff", "Caucasian Shepherd Dog", "Great Dane"], "Siamese cat": ["Bengal tiger", "Snow leopard", "Lynx", "Cheetah", "Leopard", "Jaguar", "Ocelot", "Serval", "Caracal", "Bobcat"], "horse": ["zebra", "giraffe", "cow", "deer", "camel", "elephant", "rhinoceros", "kangaroo", "donkey", "goat"], "antelope": ["gazelle", "deer", "horse", "giraffe", "zebra", "kangaroo", "llama", "camel", "goat", "sheep"], "container ship": ["cruise ship", "oil tanker", "cargo plane", "train", "semi-truck", "warehouse", "construction crane", "shipping container", "cargo truck", "cargo train"], "garbage truck": ["cement mixer", "fire truck", "tow truck", "dump truck", "delivery truck", "food truck", "ice cream truck", "moving truck", "tanker truck", "pickup truck"], "sports car": ["racehorse", "speedboat", "fighter jet", "motorcycle", "roller coaster", "rocket", "cheetah", "bullet train", "Formula 1 car", "jet ski"], "warplane": ["helicopter", "rocket", "hot air balloon", "blimp", "fighter jet", "drone", "spaceship", "glider", "airship", "paraglider"]} -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet10_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"brambling bird": ["goldfinch bird", "cardinal bird", "blue jay bird", "robin bird", "sparrow bird", "hummingbird", "parrot bird", "toucan bird", "penguin bird", "flamingo bird"], "bull frog": ["tree frog", "poison dart frog", "toad", "salamander", "lizard", "turtle", "snake", "crocodile", "alligator", "iguana"], "swiss mountain dog": ["Bernese mountain dog", "Saint Bernard", "Great Pyrenees", "Alaskan Malamute", "Siberian Husky", "Newfoundland", "Leonberger", "Tibetan Mastiff", "Caucasian Shepherd Dog", "Great Dane"], "Siamese cat": ["Bengal tiger", "Snow leopard", "Lynx", "Cheetah", "Leopard", "Jaguar", "Ocelot", "Serval", "Caracal", "Bobcat"], "horse": ["zebra", "giraffe", "cow", "deer", "camel", "elephant", "rhinoceros", "kangaroo", "donkey", "goat"], "antelope": ["gazelle", "deer", "horse", "giraffe", "zebra", "kangaroo", "llama", "camel", "goat", "sheep"], "container ship": ["cruise ship", "oil tanker", "cargo plane", "train", "semi-truck", "warehouse", "construction crane", "shipping container", "cargo truck", "cargo train"], "garbage truck": ["cement mixer", "fire truck", "tow truck", "dump truck", "delivery truck", "food truck", "ice cream truck", "moving truck", "tanker truck", "pickup truck"], "sports car": ["racehorse", "speedboat", "fighter jet", "motorcycle", "roller coaster", "rocket", "cheetah", "bullet train", "Formula 1 car", "jet ski"], "warplane": ["helicopter", "rocket", "hot air balloon", "blimp", "fighter jet", "drone", "spaceship", "glider", "airship", "paraglider"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/pet37_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Rainbow", "Aurora borealis", "Jellyfish", "Peacock", "Cherry blossom", "Autumn leaves", "Butterfly", "Elephant", "Lion", "Tiger", "Zebra", "Penguin", "Dolphin", "Whale", "Octopus", "Koala", "Kangaroo", "Cactus", "Mushroom", "Rose", "Orchid", "Tulip", "Dandelion", "Maple tree", "Oak tree", "Pine tree", "Bamboo forest", "Beach sunset", "City skyline", "Ancient ruins", "Waterfall", "Canyon", "Volcano", "Iceberg", "Desert oasis", "Lighthouse", "Hot air balloon", "Space nebula", "Galaxy", "Moon", "Starfish", "Seashell", "Coral reef- Sunset beach", "Cherry blossom", "Lavender field", "Snowy forest", "Waterfall mist", "Autumn foliage", "Mountain peak", "Desert dunes", "Rainbow sky", "Starry night", "Ocean waves", "Tropical island", "Bamboo forest", "Mossy forest", "Sunflower field", "Rolling hills", "Frozen lake", "Sandy beach", "Rocky cliffs", "Wildflower meadow", "Moss-covered rocks", "Misty mountains", "Colorful sunset", "Snow-capped peaks", "Crystal clear lake", "Vibrant coral reef", "Majestic waterfall", "Dense jungle", "Tranquil lake", "Serene river", "Majestic canyon", "Pristine beach", "Enchanting forest", "Serene lake", "Majestic mountains", "Vibrant sunset", "Peaceful river", "Tranquil beach", "Blossoming cherry trees", "Snowy landscape", "Golden sunset", "Serene ocean", "Majestic ocean", "Tranquil forest", "Vibrant sunrise", "Peaceful waterfall", "Blossoming flowers", "Snowy mountains", "Golden beach", "Serene sunset"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/ImageNet_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Sunset", "Rainbow", "Snowflake", "Aurora borealis", "Lightning", "Galaxy", "Moon", "Star", "Cloud", "Forest", "Mountain range", "Beach sunset", "Ocean wave", "Canyon", "Glacier", "Volcano eruption", "Tornado", "Hurricane", "Tsunami", "Sand dunes", "Iceberg", "Coral reef", "Waterfall", "Rainforest", "Savannah", "Desert oasis", "Arctic tundra", "Grassland", "Wetland", "Mangrove forest", "Cactus desert", "Bamboo forest", "Autumn foliage", "Cherry blossom", "Lavender field", "Tulip garden", "Sunflower field", "Wheat field", "Vineyard", "Mossy forest", "Alpine meadow", "Lush green valley", "Rocky coastline", "Sandy beach", "Snowy mountain peak", "Starry night sky- Fireworks", "City skyline", "Neon lights", "Street graffiti", "Abstract painting", "Vintage car", "Skateboard trick", "Roller coaster", "Ferris wheel", "Hot air balloon", "Kite flying", "Paragliding", "Rock climbing", "Bungee jumping", "Surfing waves", "Jet ski racing", "Snowboarding trick", "Ice skating", "Ballet performance", "Street dance", "Martial arts", "Yoga poses", "Gymnastics routine", "Archery competition", "Polo match", "Formula 1 race", "Soccer match", "Basketball dunk", "Tennis serve", "Golf swing", "Baseball pitch", "Rugby scrum", "Ice hockey game", "Volleyball spike", "Cricket match", "Horse racing", "Sailing regatta", "Kayaking adventure", "Scuba diving", "Snorkeling", "Fishing trip", "Hiking trail", "Camping scene", "Picnic in park", "Farmers market", "Food truck festival", "Wine tasting", "Cooking class", "Fashion runway show"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/car196_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Helicopter", "Hot air balloon", "Spaceship", "Tram", "Trolleybus- Motorcycle helmet", "Bicycle wheel", "Airplane wing", "Boat anchor", "Train tracks", "Bus stop", "Car keys", "Truck tire", "Van window", "SUV roof", "Convertible top", "Sedan mirror", "Coupe door", "Hatchback trunk", "Minivan sliding door", "Pickup truck bed", "Limousine interior", "Ambulance stretcher", "Fire truck ladder", "Police car siren", "Taxi meter", "Bulldozer blade", "Excavator bucket", "Crane hook", "Forklift pallet", "Tractor wheel", "Trailer truck hitch", "Garbage truck compactor", "Cement mixer drum", "Ice cream truck music", "Food truck menu", "Mail truck mailbox", "Tow truck winch", "School bus stop sign", "Double-decker bus stairs", "Tour bus microphone", "RV awning", "Motorhome kitchen", "Jet ski handlebars", "Yacht anchor", "Sailboat mast", "Speedboat propeller", "Canoe paddle", "Kayak spray skirt", "Submarine periscope", "Helicopter rotor", "Hot air balloon basket", "Spaceship thrusters", "Tram tracks", "Trolleybus wires"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/bird200_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Snowy mountain", "Beach", "Sunset", "Forest", "City skyline", "Lake", "Canyon", "Volcano", "Glacier", "Coral reef", "Wheat field", "Vineyard", "Rainforest", "Savannah", "Deserted island", "Lighthouse", "Hot air balloon", "Aurora borealis", "Castle", "Ruins", "Windmill", "Pagoda", "Taj Mahal", "Eiffel Tower", "Statue of Liberty", "Great Wall of China", "Machu Picchu", "Pyramids of Giza", "Colosseum", "Sydney Opera House", "Mount Rushmore", "Stonehenge", "Angkor Wat", "Santorini", "Venice canals", "Grand Canyon", "Niagara Falls", "Mount Everest", "Serengeti", "Amazon rainforest", "Great Barrier Reef", "Sahara Desert", "Himalayas", "Rocky Mountains", "Alps", "Andes Mountains- Tulip field", "Bamboo forest", "Cherry blossoms", "Lavender field", "Autumn leaves", "Cactus garden", "Lotus pond", "Palm trees", "Redwood forest", "Sunflower field", "Poppy field", "Dandelion field", "Wheat field", "Rice terraces", "Tea plantation", "Mossy forest", "Bamboo grove", "Moss-covered rocks", "Sand dunes", "Iceberg", "Northern lights", "Rainbow", "Thunderstorm", "Lightning strike", "Tornado", "Tsunami", "Waterfall mist", "Misty mountains", "Snowy landscape", "Desert oasis", "Coral reef", "Underwater cave", "Deep sea trench", "Ice cave", "Ancient ruins", "Medieval castle", "Gothic cathedral", "Modern skyscraper", "Floating market", "Street art", "Neon lights", "Night market", "Hot air balloons", "Paragliding", "Bungee jumping", "Skydiving", "Surfing waves", "Whitewater rafting", "Hiking trail", "Camping site", "Wildlife safari"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/pet18_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Balinese", "Oriental Shorthair", "American Shorthair", "British Shorthair", "Exotic Shorthair", "Chartreux", "Turkish Angora", "Turkish Van", "Norwegian Forest", "Siberian", "Somali", "Ocicat", "American Bobtail", "Manx", "American Curl", "Selkirk Rex", "LaPerm", "Havana Brown", "Singapura", "Egyptian Mau", "Bengal", "Savannah", "Toyger", "Pixiebob", "Ragamuffin", "Munchkin", "Sphynx", "Peterbald", "Lykoi", "Australian Mist", "Chausie", "Cheetoh", "Donskoy", "Highlander", "Kurilian Bobtail", "Nebelung", "Ojos Azules", "Serengeti", "Thai", "Ukrainian Levkoy- American Eskimo Dog", "Australian Cattle Dog", "Australian Terrier", "Basenji", "Bichon Frise", "Bloodhound", "Border Terrier", "Boston Terrier", "Boxer", "Brittany", "Brussels Griffon", "Bull Terrier", "Cairn Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chihuahua", "Chinese Crested", "Chow Chow", "Clumber Spaniel", "Cockapoo", "Collie", "Dalmatian", "Dandie Dinmont Terrier", "English Bulldog", "English Setter", "English Springer Spaniel", "French Bulldog", "German Shorthaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Goldendoodle", "Gordon Setter", "Great Dane", "Great Pyrenees", "Greyhound", "Irish Setter", "Irish Wolfhound", "Italian Greyhound", "Jack Russell Terrier", "Japanese Spitz", "Keeshond", "Kerry Blue Terrier", "King Charles Spaniel", "Komondor", "Kuvasz", "Lhasa Apso", "Maltese", "Mastiff", "Miniature Pinscher"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/pet18_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Balinese", "Oriental Shorthair", "American Shorthair", "British Shorthair", "Exotic Shorthair", "Chartreux", "Turkish Angora", "Turkish Van", "Norwegian Forest", "Siberian", "Somali", "Ocicat", "American Bobtail", "Manx", "American Curl", "Selkirk Rex", "LaPerm", "Havana Brown", "Singapura", "Egyptian Mau", "Bengal", "Savannah", "Toyger", "Pixiebob", "Ragamuffin", "Munchkin", "Sphynx", "Peterbald", "Lykoi", "Australian Mist", "Chausie", "Cheetoh", "Donskoy", "Highlander", "Kurilian Bobtail", "Serengeti", "Thai", "Ukrainian Levkoy", "Caracat", "Ojos Azules- American Eskimo Dog", "Australian Cattle Dog", "Australian Terrier", "Basenji", "Bichon Frise", "Bloodhound", "Border Terrier", "Boston Terrier", "Boxer", "Brittany Spaniel", "Bull Terrier", "Cairn Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chihuahua", "Chinese Crested", "Chow Chow", "Clumber Spaniel", "Cockapoo", "Collie", "Dalmatian", "Dandie Dinmont Terrier", "English Bulldog", "English Setter", "English Springer Spaniel", "French Bulldog", "German Shorthaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Goldendoodle", "Gordon Setter", "Great Dane", "Great Pyrenees", "Greyhound", "Irish Setter", "Irish Wolfhound", "Italian Greyhound", "Jack Russell Terrier", "Japanese Spitz", "Keeshond", "Kerry Blue Terrier", "King Charles Spaniel", "Komondor", "Kuvasz", "Lhasa Apso", "Maltese", "Mastiff", "Miniature Pinscher", "Newfoundland"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/pet37_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Dolphin", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Fireworks", "Butterfly", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China", "Machu Picchu", "Sydney Opera House", "Mount Everest", "Niagara Falls", "Grand Canyon", "Venice canals", "Santorini", "African safari", "Amazon rainforest", "Taj Mahal", "Stonehenge", "Easter Island", "Pyramids of Giza", "Colosseum", "Mount Fuji", "Petra", "Angkor Wat", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China- Waterfall", "Sunset", "Cherry Blossom", "Aurora Borealis", "Desert Oasis", "Tropical Beach", "Snowy Mountain", "Rolling Hills", "City Skyline", "Countryside Farm", "Ocean Waves", "Starry Night", "Autumn Forest", "Spring Meadow", "Rocky Coastline", "Wildflower Field", "Thunderstorm", "Iceberg", "Coral Reef", "Sand Dunes", "Bamboo Forest", "Vineyard Landscape", "Hot Air Balloon", "Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Car", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Art", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/pet37_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Dolphin", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Fireworks", "Butterfly", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China", "Machu Picchu", "Sydney Opera House", "Mount Everest", "Niagara Falls", "Grand Canyon", "Venice canals", "Santorini", "African safari", "Amazon rainforest", "Taj Mahal", "Stonehenge", "Easter Island", "Pyramids of Giza", "Colosseum", "Mount Fuji", "Petra", "Angkor Wat", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China- Waterfall", "Sunset", "Cherry Blossom", "Aurora Borealis", "Desert Oasis", "Tropical Beach", "Snowy Mountain", "Rolling Hills", "City Skyline", "Countryside Farm", "Ocean Waves", "Starry Night", "Autumn Forest", "Spring Meadow", "Rocky Coastline", "Wildflower Field", "Thunderstorm", "Iceberg", "Coral Reef", "Sand Dunes", "Bamboo Forest", "Vineyard Landscape", "Hot Air Balloon", "Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Car", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Art", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/ImageNet_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Sunset", "Rainbow", "Snowflake", "Aurora borealis", "Lightning", "Galaxy", "Moon", "Star", "Cloud", "Forest", "Mountain range", "Beach", "Ocean", "River", "Lake", "Island", "Canyon", "Cave", "Volcano", "Glacier", "Tornado", "Hurricane", "Tsunami", "Sand dunes", "Jungle", "Savannah", "Prairie", "Wetland", "Marsh", "Coral reef", "Underwater cave", "Iceberg", "Waterfall", "Hot spring", "Geothermal pool", "Lava flow", "Sandstone formation", "Rock formation", "Cactus", "Palm tree", "Bamboo forest", "Redwood forest", "Maple tree", "Pine tree", "Oak tree", "Willow tree", "Birch tree", "Cherry blossom tree- Desert oasis", "Snow-capped peak", "Rolling hills", "Tropical rainforest", "Sandy beach", "Rocky coastline", "Deep sea", "Coral atoll", "Frozen tundra", "Crashing waves", "Misty forest", "Colorful sunset", "Thunderstorm clouds", "Starry night sky", "Misty mountains", "Autumn foliage", "Wildflower meadow", "Sandy desert dunes", "Crystalline ice cave", "Vibrant coral reef", "Majestic waterfall", "Serene lake view", "Dense jungle canopy", "Tranquil riverbank", "Towering skyscrapers", "Bustling cityscape", "Ancient ruins", "Vibrant market scene", "Peaceful countryside", "Rustic farmhouse", "Quaint village", "Modern art gallery", "Abstract sculpture", "Urban graffiti wall", "Neon-lit street", "Industrial factory", "Futuristic architecture", "Zen garden", "Zen meditation room", "Zen rock formation", "Zen water feature", "Zen bamboo forest", "Zen tea ceremony", "Zen bonsai tree", "Zen incense burner", "Zen calligraphy art", "Zen meditation cushion", "Zen temple gate", "Zen garden bridge"]} -------------------------------------------------------------------------------- /eval.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | export CUDA_VISIBLE_DEVICES=0 3 | 4 | # datasets=('bird200' 'car198' 'food101' 'pet37' 'ImageNet') 5 | # task='far' 6 | # L=500 7 | 8 | # datasets=('ImageNet10' 'ImageNet20' 'ImageNet') 9 | # task='near' 10 | # L=3 11 | 12 | datasets=('cub100_ID' 'car98_ID' 'food50_ID' 'pet18_ID') 13 | task='fine_grained' 14 | L=500 15 | 16 | for dataset in "${datasets[@]}" 17 | do 18 | for i in {0..3}; do 19 | echo "Running experiment with dataset=${dataset}, iteration=${i}, model=CLIP" 20 | python3 eval_ood_detection.py \ 21 | --llm_model 'gpt-3.5-turbo-16k' \ 22 | --ood_task "${task}" \ 23 | --score_ablation "EOE" \ 24 | --L "${L}" \ 25 | --in_dataset "${dataset}" \ 26 | --score 'EOE' \ 27 | --json_number ${i} \ 28 | --model CLIP \ 29 | --CLIP_ckpt ViT-B/16 \ 30 | --beta 0.25 \ 31 | # --generate_class # You can directly comment `generate_class` if you want to use the generated classes from JSON file 32 | done 33 | done 34 | 35 | 36 | 37 | for dataset in "${datasets[@]}" 38 | do 39 | echo "Running experiment with dataset=${dataset}" 40 | python3 eval_ood_detection.py \ 41 | --ood_task "${task}" \ 42 | --in_dataset "${dataset}" \ 43 | --score 'MCM' 44 | 45 | echo "Running experiment with dataset=${dataset}" 46 | python3 eval_ood_detection.py \ 47 | --ood_task "${task}" \ 48 | --in_dataset "${dataset}" \ 49 | --score 'max-logit' 50 | 51 | echo "Running experiment with dataset=${dataset}" 52 | python3 eval_ood_detection.py \ 53 | --ood_task "${task}" \ 54 | --in_dataset "${dataset}" \ 55 | --score 'energy' \ 56 | --T 0.01 57 | done 58 | -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/pet18_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Oriental Shorthair", "Balinese", "Himalayan", "Turkish Angora", "Turkish Van", "Exotic Shorthair", "Chartreux", "Somali", "American Shorthair", "British Shorthair", "Manx", "Norwegian Forest", "Siberian", "Singapura", "Ocicat", "American Curl", "Bombay", "Havana Brown", "Egyptian Mau", "Bengal", "Savannah", "Toyger", "Pixiebob", "Ragamuffin", "Ragdoll", "Maine Coon", "Sphynx", "Scottish Fold", "Persian", "Siamese", "Abyssinian", "Russian Blue", "Burmese", "Birman", "Tonkinese", "Oriental Shorthair", "Balinese", "Himalayan", "Turkish Angora", "Turkish Van- Australian Cattle Dog", "Bernese Mountain Dog", "Bloodhound", "Border Terrier", "Bullmastiff", "Cairn Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chinese Crested", "Chow Chow", "Clumber Spaniel", "Dalmatian", "English Setter", "French Bulldog", "German Shorthaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Gordon Setter", "Great Dane", "Great Swiss Mountain Dog", "Irish Setter", "Irish Wolfhound", "Italian Greyhound", "Jack Russell Terrier", "Japanese Spitz", "Keeshond", "Kerry Blue Terrier", "Komondor", "Kuvasz", "Lhasa Apso", "Maltese", "Miniature Bull Terrier", "Newfoundland", "Nova Scotia Duck Tolling Retriever", "Old English Sheepdog", "Papillon", "Pekingese", "Pembroke Welsh Corgi", "Petit Basset Griffon Vendeen", "Pharaoh Hound", "Pointer", "Portuguese Water Dog", "Rhodesian Ridgeback", "Saluki", "Samoyed", "Schipperke", "Scottish Terrier", "Shetland Sheepdog", "Shih Tzu", "Siberian Husky", "Soft Coated Wheaten Terrier"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/cub100_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian Goose", "Ivory-billed Woodpecker", "Jamaican Tody", "Keel-billed Toucan", "Least Tern", "Masked Booby", "Northern Gannet", "Olive Warbler", "Painted Bunting", "Quail", "Red-cockaded Woodpecker", "Sanderling", "Toco Toucan", "Upland Sandpiper", "Varied Thrush", "White Ibis", "Xantus's Murrelet", "Yellow-billed Cuckoo- African Penguin", "Black-crowned Night Heron", "Cape Gannet", "Dalmatian Pelican", "Eurasian Spoonbill", "Flamingo", "Great Blue Heron", "Hoopoe", "Indian Peafowl", "Japanese Crane", "Kingfisher", "Lilac-breasted Roller", "Macaw", "Northern Fulmar", "Ostrich", "Puffin", "Quail", "Red-billed Tropicbird", "Secretary Bird", "Toucan", "Umbrellabird", "Vulture", "White Stork", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zebra Finch", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin", "Frigatebird", "Gull", "Harrier", "Ibis", "Jacana", "Kestrel", "Lark", "Moorhen", "Nightjar", "Osprey", "Pheasant", "Quetzal", "Rail", "Sandpiper", "Tern", "Upland Sandpiper", "Vireo", "Warbler", "Xantus's Hummingbird", "Yellowthroat"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/cub100_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian Goose", "Ivory-billed Woodpecker", "Jamaican Tody", "Keel-billed Toucan", "Least Tern", "Masked Booby", "Northern Gannet", "Olive Warbler", "Painted Bunting", "Quail", "Red-cockaded Woodpecker", "Sanderling", "Toco Toucan", "Upland Sandpiper", "Varied Thrush", "White Ibis", "Xantus's Murrelet", "Yellow-billed Cuckoo- African Penguin", "Black-crowned Night Heron", "Cape Gannet", "Dalmatian Pelican", "Eurasian Spoonbill", "Flamingo", "Great Blue Heron", "Hoopoe", "Indian Peafowl", "Japanese Crane", "Kingfisher", "Lilac-breasted Roller", "Macaw", "Northern Fulmar", "Ostrich", "Puffin", "Quail", "Red-billed Tropicbird", "Secretary Bird", "Toucan", "Umbrellabird", "Vulture", "White Stork", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zebra Finch", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin", "Frigatebird", "Gull", "Harrier", "Ibis", "Jacana", "Kestrel", "Lark", "Moorhen", "Nightjar", "Osprey", "Pheasant", "Quetzal", "Rail", "Sandpiper", "Tern", "Upland Sandpiper", "Vireo", "Warbler", "Xantus's Hummingbird", "Yellowthroat"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/cub100_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian Goose", "Ivory-billed Woodpecker", "Jamaican Tody", "Keel-billed Toucan", "Least Tern", "Masked Booby", "Northern Gannet", "Olive Warbler", "Painted Bunting", "Quail", "Red-cockaded Woodpecker", "Sanderling", "Toco Toucan", "Upland Sandpiper", "Varied Thrush", "White Ibis", "Xantus's Murrelet", "Yellow-billed Cuckoo- African Penguin", "Black-crowned Night Heron", "Cape Gannet", "Dalmatian Pelican", "Eurasian Spoonbill", "Flamingo", "Great Blue Heron", "Hoopoe", "Indian Peafowl", "Japanese Crane", "Kingfisher", "Lilac-breasted Roller", "Macaw", "Northern Fulmar", "Ostrich", "Puffin", "Quail", "Red-billed Tropicbird", "Secretary Bird", "Toucan", "Umbrellabird", "Vulture", "White Stork", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zebra Finch", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin", "Frigatebird", "Gull", "Harrier", "Ibis", "Jacana", "Kestrel", "Lark", "Moorhen", "Nightjar", "Osprey", "Pheasant", "Quetzal", "Rail", "Sandpiper", "Tern", "Upland Sandpiper", "Vireo", "Warbler", "Xantus's Hummingbird", "Yellowthroat"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/food50_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Pizza", "Tacos", "Sushi Burrito", "Pad Thai", "Chicken Tikka Masala", "Pho", "Ramen", "Biryani", "Falafel", "Tandoori Chicken", "Butter Chicken", "Shawarma", "Fish and Chips", "Peking Duck", "Dim Sum", "Nachos", "Guacamole", "Enchiladas", "Churros", "Empanadas", "Arepas", "Pupusas", "Ceviche", "Tamales", "Gazpacho", "Paella", "Croissant", "Baguette", "Croque Monsieur", "Quiche", "Ratatouille", "Escargots", "Bouillabaisse", "Coq au Vin", "Beef Bourguignon", "Cr\u00e8me Br\u00fbl\u00e9e", "Profiteroles", "Macarons", "Eclair", "Croquembouche", "Baklava", "Tiramisu", "Cannoli", "Gelato", "Sorbet", "Panna Cotta", "Affogato", "Churros with Chocolate Sauce", "Matcha Green Tea Ice Cream", "Mango Sticky Rice- Lobster Thermidor", "Beef Wellington", "Chicken Parmesan", "Lobster Mac and Cheese", "Chicken and Waffles", "Lobster Roll", "Lobster Bisque", "Chicken Alfredo", "Chicken Marsala", "Chicken Piccata", "Chicken Cordon Bleu", "Chicken Satay", "Chicken Shawarma", "Chicken Katsu", "Chicken Teriyaki", "Chicken Enchiladas", "Chicken Quesadilla", "Chicken Fajitas", "Chicken Tacos", "Chicken Nachos", "Chicken Caesar Salad", "Chicken Cobb Salad", "Chicken Tortilla Soup", "Chicken Noodle Soup", "Chicken Pot Pie", "Chicken Fried Rice", "Chicken Pad Thai", "Chicken Lo Mein", "Chicken Tikka Masala", "Chicken Biryani", "Chicken Curry", "Chicken Dumplings", "Chicken Spring Rolls", "Chicken Lettuce Wraps", "Chicken Gyro", "Chicken Souvlaki", "Chicken Souvlaki Pita", "Chicken Shawarma Wrap", "Chicken Shawarma Plate", "Chicken Shawarma Salad", "Chicken Shawarma Bowl", "Chicken Shawarma Pizza", "Chicken Shawarma Tacos", "Chicken Shawarma Nachos", "Chicken Shawarma Burger", "Chicken Shawarma Sandwich", "Chicken Shawarma Sliders", "Chicken Shawarma Skewers", "Chicken Shawarma Platter"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/food50_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Pizza", "Tacos", "Sushi Burrito", "Pad Thai", "Chicken Tikka Masala", "Pho", "Ramen", "Biryani", "Falafel", "Tandoori Chicken", "Shawarma", "Nachos", "Guacamole", "Enchiladas", "Chimichanga", "Empanadas", "Peking Duck", "Dim Sum", "Peking Pork", "General Tso's Chicken", "Mongolian Beef", "Bibimbap", "Bulgogi", "Kimchi", "Sashimi", "Tempura", "Udon", "Yakitori", "Miso Soup", "Soba Noodles", "Tonkatsu", "Okonomiyaki", "Takoyaki", "Matcha Ice Cream", "Bubble Tea", "Mango Sticky Rice", "Pho Bo", "Banh Mi", "Spring Rolls", "Tom Yum Soup", "Satay", "Laksa", "Hainanese Chicken Rice", "Nasi Goreng", "Roti Canai", "Curry Laksa", "Char Kway Teow", "Durian", "Mango Lassi", "Samosa- Lobster Thermidor", "Beef Wellington", "Chicken Parmesan", "Lobster Mac and Cheese", "Chicken and Waffles", "Lobster Roll", "Lobster Bisque", "Chicken Alfredo", "Chicken Marsala", "Chicken Piccata", "Chicken Cordon Bleu", "Chicken Satay", "Chicken Shawarma", "Chicken Katsu", "Chicken Teriyaki", "Chicken Enchiladas", "Chicken Quesadilla", "Chicken Fajitas", "Chicken Tacos", "Chicken Tikka", "Chicken Biryani", "Chicken Curry", "Chicken Fried Rice", "Chicken Noodle Soup", "Chicken Pot Pie", "Chicken Caesar Salad", "Chicken Cobb Salad", "Chicken Caesar Wrap", "Chicken Caesar Sandwich", "Chicken BLT Sandwich", "Chicken Club Sandwich", "Chicken and Rice", "Chicken and Dumplings", "Chicken and Waffles", "Chicken and Rice Soup", "Chicken and Mushroom Risotto", "Chicken and Broccoli Stir Fry", "Chicken and Vegetable Stir Fry", "Chicken and Cashew Stir Fry", "Chicken and Peanut Stir Fry", "Chicken and Pineapple Stir Fry", "Chicken and Mango Stir Fry", "Chicken and Black Bean Stir Fry", "Chicken and Ginger Stir Fry", "Chicken and Garlic Stir Fry", "Chicken and Sesame Stir Fry", "Chicken and Sweet and Sour Stir Fry", "Chicken and Hoisin Stir Fry", "Chicken and Oyster Sauce Stir Fry"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/bird200_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Snowy mountain", "Beach", "Forest", "Sunset", "City skyline", "Lake", "Canyon", "Volcano", "Glacier", "Coral reef", "Wheat field", "Vineyard", "Bamboo forest", "Lavender field", "Tulip garden", "Cherry blossom", "Autumn foliage", "Northern lights", "Sand dunes", "Hot air balloon", "Rainbow", "Iceberg", "Cactus", "Palm tree", "Redwood tree", "Bonsai tree", "Mossy forest", "Moss-covered rocks", "Mossy waterfall", "Mossy tree trunk", "Mossy path", "Mossy stone wall", "Mossy bridge", "Mossy log", "Mossy cave", "Mossy roof", "Mossy steps", "Mossy fence", "Mossy gate", "Mossy bench", "Mossy statue", "Mossy pond", "Mossy riverbank", "Mossy hillside", "Mossy meadow", "Mossy field", "Mossy garden", "Mossy flower bed", "Mossy shrub", "Mossy bush- Desert oasis", "Snow-capped peak", "Tropical island", "Urban skyline", "Crystal clear lake", "Deep canyon gorge", "Active volcano", "Frozen tundra", "Sandy beachscape", "Dense rainforest", "Vibrant sunset sky", "Modern cityscape", "Serene waterfall", "Majestic mountain range", "Lush green forest", "Tranquil lakefront", "Colorful coral reef", "Golden wheat field", "Vast vineyard landscape", "Zen bamboo forest", "Fragrant lavender field", "Blossoming tulip field", "Cherry blossom tree", "Vibrant autumn leaves", "Dancing northern lights", "Rolling sand dunes", "Floating hot air balloon", "Radiant rainbow arch", "Massive iceberg", "Prickly cactus plant", "Tall palm trees", "Towering redwoods", "Miniature bonsai tree", "Enchanting moss forest", "Cascading waterfall", "Ancient moss-covered rocks", "Enigmatic mossy cave", "Mossy stone pathway", "Weathered mossy bridge", "Mossy tree stump", "Mossy garden gate", "Mossy log cabin", "Mossy statue garden", "Mossy pond reflection", "Mossy riverbank scene", "Mossy hilltop view", "Mossy meadow landscape", "Mossy flower garden", "Mossy shrubbery maze", "Mossy bush cluster"]} -------------------------------------------------------------------------------- /envisioned_classes/far_100/bird200_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Snowy mountain", "Beach", "Forest", "Sunset", "City skyline", "Lake", "Canyon", "Volcano", "Glacier", "Coral reef", "Wheat field", "Vineyard", "Bamboo forest", "Lavender field", "Tulip garden", "Cherry blossom", "Autumn foliage", "Northern lights", "Sand dunes", "Hot air balloon", "Rainbow", "Iceberg", "Cactus", "Palm tree", "Redwood tree", "Bonsai tree", "Mossy forest", "Moss-covered rocks", "Mossy waterfall", "Mossy tree trunk", "Mossy path", "Mossy stone wall", "Mossy bridge", "Mossy log", "Mossy cave", "Mossy roof", "Mossy steps", "Mossy fence", "Mossy gate", "Mossy bench", "Mossy statue", "Mossy pond", "Mossy riverbank", "Mossy hillside", "Mossy meadow", "Mossy field", "Mossy garden", "Mossy flower bed", "Mossy shrub", "Mossy bush- Desert oasis", "Snow-capped peak", "Tropical island", "Urban skyline", "Crystal clear lake", "Deep canyon gorge", "Active volcano", "Frozen tundra", "Sandy beachscape", "Dense rainforest", "Vibrant sunset sky", "Modern cityscape", "Serene waterfall", "Majestic mountain range", "Lush green forest", "Tranquil lakefront", "Colorful coral reef", "Golden wheat field", "Vast vineyard landscape", "Zen bamboo forest", "Fragrant lavender field", "Blossoming tulip field", "Cherry blossom tree", "Vibrant autumn leaves", "Dancing northern lights", "Rolling sand dunes", "Floating hot air balloon", "Radiant rainbow arch", "Massive iceberg", "Prickly cactus plant", "Tall palm trees", "Towering redwoods", "Miniature bonsai tree", "Enchanting moss forest", "Cascading waterfall", "Ancient moss-covered rocks", "Whimsical mossy tree", "Mossy woodland path", "Weathered mossy stone wall", "Mossy covered bridge", "Mossy fallen log", "Mossy hidden cave", "Mossy thatched roof", "Mossy stone steps", "Mossy wooden fence", "Mossy garden gate", "Mossy wooden bench", "Mossy statue sculpture", "Mossy pond reflection", "Mossy riverbank scene", "Mossy hillside view", "Mossy wildflower meadow", "Mossy open field", "Mossy secret garden", "Mossy shrubbery maze", "Mossy bush cluster"]} -------------------------------------------------------------------------------- /utils/plot_util.py: -------------------------------------------------------------------------------- 1 | 2 | import seaborn as sns 3 | from matplotlib import pyplot as plt 4 | import numpy as np 5 | import os 6 | import pandas as pd 7 | import torch 8 | import torch.nn.functional as F 9 | from matplotlib.ticker import ScalarFormatter 10 | 11 | 12 | def plot_distribution(args, id_scores, ood_scores, out_dataset): 13 | sns.set(style="white", palette="muted") 14 | # palette = ['#A8BAE3', '#55AB83'] 15 | # palette = ['#A8BAE3', '#FF9999'] 16 | palette = ['#8E8BFE', '#FEA3A2'] 17 | sns_plt = sns.displot({"ID":-1 * id_scores, "OOD": -1 * ood_scores}, label="id", kind = "kde", 18 | palette=palette, fill = True, alpha = 0.8, linewidth=3, legend=False) 19 | # sns_plt._legend.set_bbox_to_anchor((0.85, 0.85)) 20 | plt.xticks([]) 21 | plt.yticks([]) 22 | 23 | ax = plt.gca() # 获取当前的ax对象 24 | for spine in ax.spines.values(): 25 | spine.set_linewidth(3) # 设置线条宽度为2 26 | 27 | plt.savefig(os.path.join(args.log_directory,f"{args.score}_{out_dataset}.png"), bbox_inches='tight') 28 | 29 | # def plot_distribution(args, id_scores, ood_scores, out_dataset): 30 | # sns.set(style="white", palette="muted") 31 | # palette = ['#A8BAE3', '#55AB83'] 32 | 33 | # sns_plt = sns.displot({"ID": -1 * id_scores, "OOD": -1 * ood_scores}, label="id", kind="kde", palette=palette, fill=True, alpha=0.8) 34 | # sns_plt._legend.set_bbox_to_anchor((0.85, 0.85)) 35 | 36 | # # Set x-axis to scientific notation 37 | # ax = plt.gca() # 获取当前的轴 38 | # formatter = ScalarFormatter(useMathText=True) 39 | # ax.xaxis.set_major_formatter(formatter) 40 | # ax.ticklabel_format(style='sci', scilimits=(-3,4), axis='x') 41 | 42 | # # Ensure that the font of scientific notation matches other x-axis labels 43 | # ax.xaxis.get_offset_text().set_fontsize(plt.rcParams['xtick.labelsize']) 44 | 45 | # plt.savefig(os.path.join(args.log_directory, f"{args.score}_{out_dataset}.png"), bbox_inches='tight') 46 | 47 | 48 | def show_values_on_bars(axs): 49 | def _show_on_single_plot(ax): 50 | for p in ax.patches: 51 | _x = p.get_x() + p.get_width() / 2 52 | _y = p.get_y() + p.get_height() 53 | value = '{:.2f}'.format(p.get_height()) 54 | ax.text(_x, _y, value, ha="center", fontsize=9) 55 | if isinstance(axs, np.ndarray): 56 | for idx, ax in np.ndenumerate(axs): 57 | _show_on_single_plot(ax) 58 | else: 59 | _show_on_single_plot(axs) 60 | 61 | 62 | -------------------------------------------------------------------------------- /utils/args_pool.py: -------------------------------------------------------------------------------- 1 | ALL_ID_DATASET = [ 2 | # far task 3 | 'cifar10', 'cifar100', 'bird200', 'car196', 'food101', 'pet37', 'ImageNet', 'ImageNet_sketch', 4 | # near task 5 | 'ImageNet10', 'ImageNet20', 6 | # fine_grained task 7 | 'cub100_ID', 'car98_ID', 'food50_ID', 'pet18_ID', 8 | # explore the robustness 9 | 'ImageNet_C_blur_defocus_blur', 'ImageNet_C_blur_glass_blur', 'ImageNet_C_blur_motion_blur', 'ImageNet_C_blur_zoom_blur', 10 | 'ImageNet_C_digital_contrast', 'ImageNet_C_digital_elastic_transform', 'ImageNet_C_digital_jpeg_compression', 'ImageNet_C_digital_pixelate', 11 | 'ImageNet_C_extra_gaussian_blur', 'ImageNet_C_extra_saturate', 'ImageNet_C_extra_spatter', 'ImageNet_C_extra_speckle_noise', 12 | 'ImageNet_C_noise_gaussian_noise', 'ImageNet_C_noise_impulse_noise', 'ImageNet_C_noise_shot_noise', 13 | 'ImageNet_C_weather_brightness', 'ImageNet_C_weather_fog', 'ImageNet_C_weather_frost', 'ImageNet_C_weather_snow'] 14 | 15 | 16 | ALL_OOD_TASK = [ 17 | # main results 18 | 'far', 'near', 'fine_grained', 19 | # general prompt (Limitation II in paper) 20 | 'general', 21 | # below is the ablation studies for LLM prompts 22 | 'fine_grained_irrelevant', 'fine_grained_dissimilar', 23 | 'near_irrelevant', 'near_dissimilar', 24 | 'far_irrelevant', 'far_dissimilar'] 25 | 26 | 27 | ALL_LLM = [ 28 | 'gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-4', 'gpt-4-1106-preview', 'gpt-4-0125-preview', 29 | 'Claude-2', 'Claude-2-100k', 'Claude-3-Haiku', 30 | # NOTE: Llama's responses may not adhere strictly to the predefined JSON format, thus we manually input the output of llama into JSON in the ablation experiment. 31 | 'Llama-2-7b', 'Llama-2-13b', 'Llama-2-70b', 32 | 'Mixtral-8x7B-Chat', 'Gemma-7b-FW', 'Gemini-Pro'] 33 | 34 | 35 | dataset_mappings = { 36 | # far ood 37 | 'bird200': ['iNaturalist', 'SUN', 'places365', 'dtd'], 38 | 'car196': ['iNaturalist', 'SUN', 'places365', 'dtd'], 39 | 'food101': ['iNaturalist', 'SUN', 'places365', 'dtd'], 40 | 'pet37': ['iNaturalist', 'SUN', 'places365', 'dtd'], 41 | 'ImageNet_sketch': ['iNaturalist', 'SUN', 'places365', 'dtd'], 42 | 'cifar10': ['svhn', 'lsun', 'dtd', 'places365'], 43 | 'cifar100': ['svhn', 'lsun', 'dtd', 'places365'], 44 | # near ood 45 | 'ImageNet10': ['ImageNet20'], 46 | 'ImageNet20': ['ImageNet10'], 47 | # fine-grained ood 48 | 'cub100_ID': ['cub100_OOD'], 49 | 'car98_ID': ['car98_OOD'], 50 | 'food50_ID': ['food50_OOD'], 51 | 'pet18_ID': ['pet18_OOD'], 52 | } -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet20_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog", "chameleon", "axolotl", "seahorse", "jellyfish", "starfish", "snail", "butterfly", "hummingbird", "squirrel"], "eft": ["lizard", "gecko", "chameleon", "salamander", "newt", "frog", "toad", "snake", "iguana", "turtle"], "spotted salamander": ["leopard", "cheetah", "giraffe", "jaguar", "dalmatian", "cow", "ladybug", "butterfly", "snake", "zebra"], "green lizard": ["tree frog", "chameleon", "iguana", "gecko", "anole", "basilisk", "dragonfly", "grasshopper", "praying mantis", "leaf insect"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard", "iguana", "turtle", "snake", "shark", "hippopotamus", "rhinoceros", "elephant"], "timber wolf": ["gray wolf", "red fox", "coyote", "arctic fox", "dingo", "jackal", "African wild dog", "hyena", "raccoon dog", "maned wolf"], "Arctic fox": ["Snowy owl", "Polar bear", "Snowshoe hare", "Snow leopard", "Arctic tern", "Walrus", "Beluga whale", "Iceberg", "Northern lights", "Igloo"], "brown bear": ["grizzly bear", "polar bear", "black bear", "panda bear", "koala bear", "teddy bear", "sloth bear", "sun bear", "spectacled bear", "Andean bear"], "starfish": ["sea urchin", "jellyfish", "coral", "seashell", "sea anemone", "sea cucumber", "sea sponge", "sea snail", "sea horse", "sea turtle"], "zebra": ["horse", "giraffe", "cow", "donkey", "deer", "antelope", "camel", "llama", "kangaroo", "panda"], "balloon": ["bubbles", "jellyfish", "hot air balloon", "parachute", "beach ball", "soap foam", "fireworks", "inflatable pool toy", "party hat", "confetti"], "bullet train": ["roller coaster", "race car", "airplane", "rocket", "speedboat", "subway train", "monorail", "cable car", "tram", "maglev train"], "canoe": ["kayak", "paddleboard", "rowboat", "sailboat", "raft", "fishing boat", "speedboat", "yacht", "submarine", "hovercraft"], "missile": ["rocket", "airplane", "submarine", "bullet", "torpedo", "cannon", "tank", "drone", "satellite", "spaceship"], "moped": ["bicycle", "scooter", "motorcycle", "skateboard", "rollerblades", "wheelchair", "shopping cart", "golf cart", "Segway", "electric scooter"], "sailboat": ["hot air balloon", "lighthouse", "surfboard", "windmill", "kayak", "submarine", "seagull", "palm tree", "beach umbrella", "snorkel gear"], "snowmobile": ["motorcycle", "tractor", "jet ski", "bulldozer", "speedboat", "forklift", "race car", "helicopter", "bicycle", "skateboard"], "space shuttle": ["airplane", "rocket", "hot air balloon", "submarine", "train", "car", "motorcycle", "bicycle", "boat", "helicopter"], "steam locomotive": ["vintage car", "sailboat", "hot air balloon", "airplane", "tractor", "motorcycle", "bicycle", "roller coaster", "subway train", "cruise ship"], "tank": ["armored vehicle", "military aircraft", "battleship", "construction vehicle", "submarine", "bulldozer", "crane", "excavator", "forklift", "tractor"]} -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet20_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog", "chameleon", "axolotl", "seahorse", "jellyfish", "starfish", "snail", "butterfly", "hummingbird", "squirrel"], "eft": ["lizard", "gecko", "chameleon", "salamander", "newt", "frog", "toad", "snake", "iguana", "turtle"], "spotted salamander": ["leopard", "cheetah", "giraffe", "jaguar", "dalmatian", "cow", "ladybug", "butterfly", "snake", "zebra"], "green lizard": ["green tree frog", "emerald tree boa", "green iguana", "green anole", "green mamba", "green sea turtle", "green parrot", "green leaf", "green apple", "green grass"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard", "iguana", "turtle", "snake", "shark", "hippopotamus", "rhinoceros", "elephant"], "timber wolf": ["gray wolf", "red fox", "coyote", "arctic fox", "dingo", "jackal", "hyena", "African wild dog", "maned wolf", "fennec fox"], "Arctic fox": ["Snowy owl", "Polar bear", "Snowshoe hare", "Snow leopard", "Arctic tern", "Walrus", "Beluga whale", "Iceberg", "Northern lights", "Igloo"], "brown bear": ["grizzly bear", "polar bear", "black bear", "panda bear", "koala bear", "teddy bear", "sloth bear", "sun bear", "spectacled bear", "Andean bear"], "starfish": ["sea urchin", "jellyfish", "coral", "seashell", "sea anemone", "sea cucumber", "sea sponge", "sea snail", "sea horse", "sea turtle"], "zebra": ["giraffe", "cow", "horse", "donkey", "tiger", "leopard", "cheetah", "panda", "dalmatian", "jaguar"], "balloon": ["bubbles", "jellyfish", "hot air balloon", "parachute", "beach ball", "soap foam", "fireworks", "inflatable pool toy", "party hat", "confetti"], "bullet train": ["roller coaster", "race car", "airplane", "rocket", "speedboat", "subway train", "monorail", "cable car", "tram", "maglev train"], "canoe": ["kayak", "paddleboard", "sailboat", "raft", "rowboat", "yacht", "fishing boat", "speedboat", "gondola", "dinghy"], "missile": ["rocket", "airplane", "submarine", "bullet", "torpedo", "cannon", "tank", "drone", "satellite", "spaceship"], "moped": ["bicycle", "scooter", "motorcycle", "skateboard", "rollerblades", "wheelchair", "shopping cart", "golf cart", "Segway", "electric scooter"], "sailboat": ["hot air balloon", "lighthouse", "surfboard", "windmill", "kayak", "submarine", "seagull", "palm tree", "beach umbrella", "snorkel gear"], "snowmobile": ["motorcycle", "tractor", "jet ski", "bulldozer", "speedboat", "forklift", "race car", "helicopter", "bicycle", "skateboard"], "space shuttle": ["airplane", "rocket", "hot air balloon", "submarine", "cruise ship", "train", "car", "motorcycle", "bicycle", "skateboard"], "steam locomotive": ["vintage car", "sailboat", "hot air balloon", "airplane", "tractor", "motorcycle", "bicycle", "roller coaster", "subway train", "cruise ship"], "tank": ["armored vehicle", "military aircraft", "battleship", "construction vehicle", "submarine", "bulldozer", "crane", "excavator", "forklift", "tractor"]} -------------------------------------------------------------------------------- /envisioned_classes/near_10/ImageNet20_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"common newt": ["poison dart frog", "chameleon", "axolotl", "seahorse", "jellyfish", "starfish", "snail", "butterfly", "hummingbird", "squirrel"], "eft": ["lizard", "gecko", "chameleon", "salamander", "newt", "frog", "toad", "snake", "iguana", "turtle"], "spotted salamander": ["leopard", "cheetah", "giraffe", "jaguar", "dalmatian", "cow", "ladybug", "butterfly", "snake", "zebra"], "green lizard": ["green tree frog", "emerald tree boa", "green iguana", "green anole", "green mamba", "green sea turtle", "green parrot", "green leaf", "green apple", "green grass"], "African crocodile": ["alligator", "komodo dragon", "monitor lizard", "iguana", "turtle", "snake", "shark", "hippopotamus", "rhinoceros", "elephant"], "timber wolf": ["gray wolf", "red fox", "coyote", "arctic fox", "dingo", "jackal", "African wild dog", "hyena", "raccoon dog", "maned wolf"], "Arctic fox": ["Snowy owl", "Polar bear", "Snowshoe hare", "Snow leopard", "Arctic tern", "Walrus", "Beluga whale", "Iceberg", "Northern lights", "Igloo"], "brown bear": ["grizzly bear", "polar bear", "black bear", "panda bear", "koala bear", "teddy bear", "sloth bear", "sun bear", "spectacled bear", "Andean bear"], "starfish": ["sea urchin", "jellyfish", "coral", "seashell", "sea anemone", "sea cucumber", "sea sponge", "sea snail", "sea horse", "sea turtle"], "zebra": ["giraffe", "cow", "horse", "donkey", "tiger", "leopard", "cheetah", "panda", "dalmatian", "jaguar"], "balloon": ["bubbles", "jellyfish", "hot air balloon", "parachute", "beach ball", "soap foam", "fireworks", "inflatable pool toy", "party hat", "confetti"], "bullet train": ["roller coaster", "race car", "airplane", "rocket", "speedboat", "subway train", "monorail", "cable car", "tram", "maglev train"], "canoe": ["kayak", "paddleboard", "sailboat", "raft", "rowboat", "yacht", "fishing boat", "speedboat", "gondola", "dinghy"], "missile": ["rocket", "airplane", "submarine", "bullet", "torpedo", "cannon", "tank", "drone", "satellite", "spaceship"], "moped": ["bicycle", "scooter", "motorcycle", "skateboard", "rollerblades", "wheelchair", "shopping cart", "golf cart", "Segway", "electric scooter"], "sailboat": ["hot air balloon", "lighthouse", "surfboard", "windmill", "kayak", "submarine", "seagull", "palm tree", "beach umbrella", "snorkel gear"], "snowmobile": ["motorcycle", "tractor", "jet ski", "bulldozer", "speedboat", "forklift", "race car", "helicopter", "bicycle", "skateboard"], "space shuttle": ["airplane", "rocket", "hot air balloon", "submarine", "cruise ship", "train", "car", "motorcycle", "bicycle", "skateboard"], "steam locomotive": ["vintage car", "sailboat", "hot air balloon", "airplane", "tractor", "motorcycle", "subway train", "roller coaster", "cruise ship", "rocket"], "tank": ["armored vehicle", "military aircraft", "battleship", "construction vehicle", "submarine", "bulldozer", "crane", "excavator", "forklift", "tractor"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/car98_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Ford Mustang Coupe 2012", "Nissan 370Z Coupe 2012", "Subaru Impreza Sedan 2012", "Honda Civic Coupe 2012", "Kia Optima Sedan 2012", "Mazda MX-5 Miata Convertible 2012", "Volkswagen Beetle Hatchback 2012", "Toyota Yaris Hatchback 2012", "Chevrolet Sonic Sedan 2012", "Hyundai Genesis Coupe 2012", "Mitsubishi Outlander SUV 2012", "Jeep Wrangler SUV 2012", "Ford Focus Sedan 2012", "Nissan Versa Sedan 2012", "Subaru Legacy Sedan 2012", "Honda Fit Hatchback 2012", "Kia Soul Hatchback 2012", "Mazda Mazda3 Sedan 2012", "Volkswagen Jetta Sedan 2012", "Toyota Prius Hatchback 2012", "Chevrolet Malibu Sedan 2012", "Hyundai Elantra Sedan 2012", "Mitsubishi Eclipse Coupe 2012", "Jeep Compass SUV 2012", "Ford Fusion Sedan 2012", "Nissan Sentra Sedan 2012", "Subaru Forester SUV 2012", "Honda CR-V SUV 2012", "Kia Sportage SUV 2012", "Mazda Mazda6 Sedan 2012", "Volkswagen Passat Sedan 2012", "Toyota Camry Hybrid Sedan 2012", "Chevrolet Cruze Sedan 2012", "Hyundai Sonata Hybrid Sedan 2012", "Mitsubishi Galant Sedan 2012", "Jeep Patriot SUV 2012", "Ford Taurus Sedan 2012", "Nissan Altima Sedan 2012", "Subaru Outback Wagon 2012", "Honda Accord Coupe 2012", "Kia Rio Sedan 2012", "Mazda CX-9 SUV 2012", "Volkswagen Tiguan SUV 2012", "Toyota Highlander SUV 2012", "Chevrolet Equinox SUV 2012", "Hyundai Santa Fe Sport SUV 2012", "Mitsubishi Lancer Evolution Sedan 2012", "Jeep Renegade SUV 2012", "Ford Escape SUV 2012", "Nissan Maxima Sedan 2012", "Subaru XV Crosstrek SUV 2012", "Honda Pilot SUV 2012- Audi A3 Sedan 2012", "BMW 5 Series Sedan 2012", "Cadillac ATS Sedan 2012", "Chevrolet Camaro Convertible 2012", "Dodge Dart Sedan 2012", "Ford Edge SUV 2012", "GMC Terrain SUV 2012", "Honda Ridgeline Crew Cab 2012", "Hyundai Equus Sedan 2012", "Infiniti G Coupe IPL 2012", "Jaguar XF Sedan 2012", "Kia Forte Sedan 2012", "Lexus IS 350 Sedan 2012", "Mazda CX-5 SUV 2012", "Mercedes-Benz C-Class Sedan 2012", "Nissan Juke Hatchback 2012", "Porsche Panamera Sedan 2012", "Subaru BRZ Coupe 2012", "Toyota FJ Cruiser SUV 2012", "Volkswagen Touareg SUV 2012", "Audi A4 Sedan 2012", "BMW 7 Series Sedan 2012", "Cadillac CTS Coupe 2012", "Chevrolet Corvette Convertible 2012", "Dodge Durango SUV 2012", "Ford Expedition SUV 2012", "GMC Yukon SUV 2012", "Honda S2000 Convertible 2009", "Hyundai Genesis Sedan 2012", "Infiniti QX56 SUV 2012", "Jaguar XJ Sedan 2012", "Kia Rio Hatchback 2012", "Lexus RX 350 SUV 2012", "Mazda CX-7 SUV 2012", "Mercedes-Benz E-Class Coupe 2012", "Nissan Leaf Hatchback 2012", "Porsche Boxster Convertible 2012", "Subaru Tribeca SUV 2012", "Toyota Land Cruiser SUV 2012", "Volkswagen Beetle Convertible 2012", "Audi A5 Coupe 2012", "BMW M5 Sedan 2010", "Cadillac CTS-V Coupe 2012", "Chevrolet Cruze Hatchback 2012", "Dodge Grand Caravan Minivan 2012", "Ford Explorer SUV 2012", "GMC Yukon XL SUV 2012", "Honda CR-Z Hatchback 2012", "Hyundai Veloster Hatchback 2012", "Infiniti EX SUV 2012"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/car98_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Ford Mustang Coupe 2012", "Nissan 370Z Coupe 2012", "Subaru Impreza Sedan 2012", "Honda Civic Coupe 2012", "Kia Optima Sedan 2012", "Mazda MX-5 Miata Convertible 2012", "Volkswagen Beetle Hatchback 2012", "Toyota Yaris Hatchback 2012", "Chevrolet Sonic Sedan 2012", "Hyundai Genesis Coupe 2012", "Mitsubishi Outlander SUV 2012", "Jeep Wrangler SUV 2012", "Ford Focus Sedan 2012", "Nissan Versa Sedan 2012", "Subaru Legacy Sedan 2012", "Honda Fit Hatchback 2012", "Kia Soul Hatchback 2012", "Mazda Mazda3 Sedan 2012", "Volkswagen Jetta Sedan 2012", "Toyota Prius Hatchback 2012", "Chevrolet Malibu Sedan 2012", "Hyundai Elantra Sedan 2012", "Mitsubishi Eclipse Coupe 2012", "Jeep Compass SUV 2012", "Ford Fusion Sedan 2012", "Nissan Sentra Sedan 2012", "Subaru Forester SUV 2012", "Honda CR-V SUV 2012", "Kia Sportage SUV 2012", "Mazda Mazda6 Sedan 2012", "Volkswagen Passat Sedan 2012", "Toyota Camry Hybrid Sedan 2012", "Chevrolet Cruze Sedan 2012", "Hyundai Sonata Hybrid Sedan 2012", "Mitsubishi Galant Sedan 2012", "Jeep Patriot SUV 2012", "Ford Taurus Sedan 2012", "Nissan Altima Sedan 2012", "Subaru Outback Wagon 2012", "Honda Accord Coupe 2012", "Kia Rio Sedan 2012", "Mazda CX-9 SUV 2012", "Volkswagen Tiguan SUV 2012", "Toyota Highlander SUV 2012", "Chevrolet Equinox SUV 2012", "Hyundai Santa Fe Sport SUV 2012", "Mitsubishi Lancer Evolution Sedan 2012", "Jeep Renegade SUV 2012", "Ford Escape SUV 2012", "Nissan Maxima Sedan 2012", "Subaru XV Crosstrek SUV 2012", "Honda Pilot SUV 2012- Audi A3 Sedan 2012", "BMW 5 Series Sedan 2012", "Cadillac ATS Sedan 2012", "Chevrolet Camaro Convertible 2012", "Dodge Dart Sedan 2012", "Ford Edge SUV 2012", "GMC Terrain SUV 2012", "Honda Ridgeline Crew Cab 2012", "Hyundai Equus Sedan 2012", "Infiniti G Coupe IPL 2012", "Jaguar XF Sedan 2012", "Kia Forte Sedan 2012", "Lexus IS 350 Sedan 2012", "Mazda CX-5 SUV 2012", "Mercedes-Benz C-Class Sedan 2012", "Nissan Juke Hatchback 2012", "Porsche Panamera Sedan 2012", "Subaru BRZ Coupe 2012", "Toyota FJ Cruiser SUV 2012", "Volkswagen Touareg SUV 2012", "Audi A4 Sedan 2012", "BMW 7 Series Sedan 2012", "Cadillac CTS Coupe 2012", "Chevrolet Corvette Convertible 2012", "Dodge Durango SUV 2012", "Ford Expedition SUV 2012", "GMC Yukon SUV 2012", "Honda S2000 Convertible 2009", "Hyundai Genesis Sedan 2012", "Infiniti QX56 SUV 2012", "Jaguar XJ Sedan 2012", "Kia Rio Hatchback 2012", "Lexus RX 350 SUV 2012", "Mazda CX-7 SUV 2012", "Mercedes-Benz E-Class Coupe 2012", "Nissan Leaf Hatchback 2012", "Porsche Boxster Convertible 2012", "Subaru Tribeca SUV 2012", "Toyota Land Cruiser SUV 2012", "Volkswagen Beetle Convertible 2012", "Audi A5 Coupe 2012", "BMW M5 Sedan 2010", "Cadillac CTS-V Coupe 2012", "Chevrolet Cruze Hatchback 2012", "Dodge Grand Caravan Minivan 2012", "Ford Explorer SUV 2012", "GMC Sierra 1500 Crew Cab 2012", "Honda CR-Z Hatchback 2012", "Hyundai Sonata Hybrid Sedan 2012", "Infiniti G Convertible 2012", "Jaguar XK Coupe 2012"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_100/car98_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Ford Mustang Coupe 2012", "Nissan 370Z Coupe 2012", "Subaru Impreza Sedan 2012", "Honda Civic Coupe 2012", "Kia Optima Sedan 2012", "Mazda MX-5 Miata Convertible 2012", "Volkswagen Beetle Hatchback 2012", "Toyota Prius Hatchback 2012", "Chevrolet Camaro Convertible 2012", "Ford Focus Sedan 2012", "Nissan Versa Sedan 2012", "Subaru Outback Wagon 2012", "Honda Fit Hatchback 2012", "Kia Soul Hatchback 2012", "Mazda Mazda3 Sedan 2012", "Volkswagen Jetta Sedan 2012", "Toyota Yaris Hatchback 2012", "Chevrolet Sonic Sedan 2012", "Ford Fusion Sedan 2012", "Nissan Sentra Sedan 2012", "Subaru Legacy Sedan 2012", "Honda Accord Coupe 2012", "Kia Rio Sedan 2012", "Mazda Mazda6 Sedan 2012", "Volkswagen Passat Sedan 2012", "Toyota Camry Hybrid Sedan 2012", "Chevrolet Malibu Sedan 2012", "Ford Taurus Sedan 2012", "Nissan Altima Sedan 2012", "Subaru Forester SUV 2012", "Honda CR-V SUV 2012", "Kia Sportage SUV 2012", "Mazda CX-9 SUV 2012", "Volkswagen Tiguan SUV 2012", "Toyota Highlander SUV 2012", "Chevrolet Equinox SUV 2012", "Ford Escape SUV 2012", "Nissan Rogue SUV 2012", "Subaru XV Crosstrek SUV 2012", "Honda Pilot SUV 2012", "Kia Sorento SUV 2012", "Mazda CX-5 SUV 2012", "Volkswagen Touareg SUV 2012", "Toyota 4Runner SUV 2012", "Chevrolet Tahoe SUV 2012", "Ford Expedition SUV 2012", "Nissan Armada SUV 2012", "Subaru Tribeca SUV 2012", "Honda Ridgeline Crew Cab 2012", "Kia Sedona Minivan 2012", "Mazda Mazda5 Minivan 2012", "Volkswagen Routan Minivan 2012", "Toyota Sienna Minivan 2012- Aston Martin DB11 Coupe 2019", "Bentley Bentayga SUV 2019", "Bugatti Chiron Coupe 2019", "Ferrari 488 GTB Coupe 2019", "Lamborghini Huracan Coupe 2019", "McLaren 720S Coupe 2019", "Porsche 911 Carrera Coupe 2019", "Rolls-Royce Cullinan SUV 2019", "Audi RS5 Coupe 2019", "BMW M5 Sedan 2019", "Mercedes-Benz AMG GT Coupe 2019", "Jaguar F-Type Coupe 2019", "Lexus LC Coupe 2019", "Maserati GranTurismo Coupe 2019", "Alfa Romeo Giulia Sedan 2019", "Aston Martin Vantage Coupe 2019", "Bentley Continental GT Coupe 2019", "Bugatti Veyron Coupe 2019", "Ferrari 812 Superfast Coupe 2019", "Lamborghini Aventador Coupe 2019", "McLaren 570S Coupe 2019", "Porsche Panamera Sedan 2019", "Rolls-Royce Ghost Sedan 2019", "Audi R8 Coupe 2019", "BMW i8 Coupe 2019", "Mercedes-Benz S-Class Sedan 2019", "Jaguar XJ Sedan 2019", "Lexus LS Sedan 2019", "Maserati Quattroporte Sedan 2019", "Alfa Romeo Stelvio SUV 2019", "Aston Martin DBS Superleggera Coupe 2019", "Bentley Flying Spur Sedan 2019", "Bugatti Veyron Super Sport Coupe 2019", "Ferrari Portofino Convertible 2019", "Lamborghini Huracan Spyder Convertible 2019", "McLaren 600LT Coupe 2019", "Porsche Cayenne SUV 2019", "Rolls-Royce Wraith Coupe 2019", "Audi RS7 Sportback Sedan 2019", "BMW M8 Coupe 2019", "Mercedes-Benz AMG GT Roadster Convertible 2019", "Jaguar F-Pace SUV 2019", "Lexus RX SUV 2019", "Maserati Levante SUV 2019", "Alfa Romeo 4C Coupe 2019", "Aston Martin Rapide Sedan 2019", "Bentley Mulsanne Sedan 2019", "Bugatti Divo Coupe 2019", "Ferrari F8 Tributo Coupe 2019", "Lamborghini Aventador SVJ Coupe 2019"]} -------------------------------------------------------------------------------- /utils/file_ops.py: -------------------------------------------------------------------------------- 1 | import os 2 | import shutil 3 | import numpy as np 4 | import logging 5 | import pandas as pd 6 | 7 | 8 | def save_scores(args, scores, dataset_name): 9 | with open(os.path.join(args.log_directory, f'{dataset_name}_scores.npy'), 'wb') as f: 10 | np.save(f, scores) 11 | 12 | 13 | def load_scores(args, dataset_name): 14 | with open(os.path.join(args.log_directory, f'{dataset_name}_scores.npy'), 'rb') as f: 15 | scores = np.load(f) 16 | return scores 17 | 18 | 19 | def setup_log(args): 20 | log = logging.getLogger(__name__) 21 | formatter = logging.Formatter('%(asctime)s : %(message)s') 22 | fileHandler = logging.FileHandler(os.path.join(args.log_directory, "ood_eval_info.log"), mode='w') 23 | fileHandler.setFormatter(formatter) 24 | streamHandler = logging.StreamHandler() 25 | streamHandler.setFormatter(formatter) 26 | log.setLevel(logging.DEBUG) 27 | log.addHandler(fileHandler) 28 | log.addHandler(streamHandler) 29 | log.debug(f"#########{args.name}############") 30 | return log 31 | 32 | 33 | def get_next_save_number(args, folder_path): 34 | files = os.listdir(folder_path) 35 | 36 | numbers = [] 37 | for file in files: 38 | if file.startswith(f'beta_{args.beta}_L_{args.L}') and file.endswith(".csv"): 39 | try: 40 | number = int(file.split('_')[-1].split('.')[0]) 41 | numbers.append(number) 42 | except ValueError: 43 | pass 44 | 45 | return 0 if not numbers else max(numbers) + 1 46 | 47 | 48 | def save_as_dataframe(args, out_datasets, fpr_list, auroc_list, aupr_list): 49 | fpr_list = [float('{:.2f}'.format(100*fpr)) for fpr in fpr_list] 50 | auroc_list = [float('{:.2f}'.format(100*auroc)) for auroc in auroc_list] 51 | aupr_list = [float('{:.2f}'.format(100*aupr)) for aupr in aupr_list] 52 | import pandas as pd 53 | data = {k:v for k,v in zip(out_datasets, zip(fpr_list,auroc_list,aupr_list))} 54 | data['AVG'] = [np.mean(fpr_list),np.mean(auroc_list),np.mean(aupr_list) ] 55 | data['AVG'] = [float('{:.2f}'.format(metric)) for metric in data['AVG']] 56 | # Specify orient='index' to create the DataFrame using dictionary keys as rows 57 | df = pd.DataFrame.from_dict(data, orient='index', 58 | columns=['FPR95', 'AUROC', 'AUPR']) 59 | df.to_csv(os.path.join(args.log_directory,f'{args.name}_{args.json_number}.csv')) 60 | 61 | 62 | def create_ImageNet_subset(src, dst, target_dirs): 63 | assert(os.path.exists(src)) 64 | if not os.path.exists(dst): 65 | os.makedirs(dst) 66 | types = ['train', 'val'] 67 | for type in types: 68 | for dir_name in os.listdir(os.path.join(src, type)): 69 | if dir_name in target_dirs: 70 | shutil.copytree(os.path.join(src, type, dir_name), os.path.join(dst,type, dir_name)) 71 | 72 | 73 | def prepare_dataframe(captions_dir = 'gen_captions', dataset_name = 'imagenet_val', multiple = False): 74 | # load caption file 75 | captions_path = os.path.join(captions_dir, f'{dataset_name}_captions.tsv') 76 | df = pd.read_csv(f"{captions_path}", sep='\t') 77 | df.columns = ["image_id","caption","cls"] 78 | if multiple: # in case a single img has multiple captions 79 | x = list(set(df['image_id'].values)) 80 | image_ids = np.arange(0, len(x)) 81 | train_images = [x[i] for i in image_ids] 82 | df = df[df["image_id"].isin(train_images)].reset_index(drop=True) 83 | return df -------------------------------------------------------------------------------- /utils/common.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | import os 4 | import numpy as np 5 | import json 6 | import random 7 | import ast 8 | import logging 9 | 10 | 11 | def setup_seed(seed): 12 | torch.manual_seed(seed) 13 | torch.cuda.manual_seed(seed) 14 | np.random.seed(seed) 15 | random.seed(seed) 16 | 17 | 18 | def get_test_labels(args, loader = None): 19 | if args.in_dataset.startswith("ImageNet_C") or args.in_dataset in ["ImageNet", "ImageNet_sketch"]: 20 | test_labels = obtain_ImageNet_classes() 21 | elif args.in_dataset == "ImageNet10": 22 | test_labels = obtain_ImageNet10_classes() 23 | elif args.in_dataset == "ImageNet20": 24 | test_labels = obtain_ImageNet20_classes() 25 | elif args.in_dataset in ['bird200', 'car196', 'food101','pet37', 26 | 'cub100_ID', 'car98_ID', 'food50_ID', 'pet18_ID']: 27 | test_labels = loader.dataset.class_names_str 28 | elif args.in_dataset in ['cifar10', 'cifar100']: 29 | test_labels = loader.dataset.classes 30 | return test_labels 31 | 32 | 33 | def obtain_ImageNet_classes(): 34 | loc = os.path.join('data', 'ImageNet') 35 | with open(os.path.join(loc, 'imagenet_class_clean.npy'), 'rb') as f: 36 | imagenet_cls = np.load(f) 37 | return imagenet_cls 38 | 39 | 40 | def obtain_ImageNet10_classes(): 41 | class_dict = {"warplane": "n04552348", "sports car": "n04285008", 42 | 'brambling bird': 'n01530575', "Siamese cat": 'n02123597', 43 | 'antelope': 'n02422699', 'swiss mountain dog': 'n02107574', 44 | "bull frog": "n01641577", 'garbage truck': "n03417042", 45 | "horse": "n02389026", "container ship": "n03095699"} 46 | # sort by values 47 | class_dict = {k: v for k, v in sorted( 48 | class_dict.items(), key=lambda item: item[1])} 49 | return class_dict.keys() 50 | 51 | 52 | def obtain_ImageNet20_classes(): 53 | class_dict = {"n04147183": "sailboat", "n02951358": "canoe", "n02782093": "balloon", "n04389033": "tank", "n03773504": "missile", 54 | "n02917067": "bullet train", "n02317335": "starfish", "n01632458": "spotted salamander", "n01630670": "common newt", "n01631663": "eft", 55 | "n02391049": "zebra", "n01693334": "green lizard", "n01697457": "African crocodile", "n02120079": "Arctic fox", "n02114367": "timber wolf", 56 | "n02132136": "brown bear", "n03785016": "moped", "n04310018": "steam locomotive", "n04266014": "space shuttle", "n04252077": "snowmobile"} 57 | # sort by values 58 | class_dict = {k: v for k, v in sorted( 59 | class_dict.items(), key=lambda item: item[0])} 60 | return class_dict.values() 61 | 62 | 63 | def get_num_cls(args): 64 | if args.in_dataset.startswith('ImageNet_C'): 65 | return 1000 66 | else: 67 | NUM_CLS_DICT = { 68 | 'bird200':200, 69 | 'car196': 196, 70 | 'food101': 101, 71 | 'pet37': 37, 72 | 'ImageNet10': 10, 73 | 'ImageNet20': 20, 74 | 'ImageNet': 1000, 75 | 'ImageNet_sketch': 1000, 76 | 'cub100_ID':100, 77 | 'cub100_OOD':100, 78 | 'car98_ID': 98, 79 | 'car98_OOD': 98, 80 | 'food50_ID': 50, 81 | 'food50_OOD': 51, 82 | 'pet18_ID': 18, 83 | 'pet18_OOD': 19, 84 | 'cifar10': 10, 85 | 'cifar100': 100, 86 | } 87 | n_cls = NUM_CLS_DICT[args.in_dataset] 88 | return n_cls -------------------------------------------------------------------------------- /envisioned_classes/far_300/car196_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Helicopter", "Hot air balloon", "Jet", "Spaceship- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Jet engine", "Propeller", "Wind turbine", "Solar panel", "Satellite", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight", "Lantern", "Compass", "Map", "GPS", "Stopwatch", "Thermometer", "Barometer", "Compass", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Jet engine", "Propeller", "Wind turbine", "Solar panel", "Satellite", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight", "Lantern", "Compass", "Map", "GPS", "Stopwatch", "Thermometer", "Barometer", "Compass", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Jet engine", "Propeller", "Wind turbine", "Solar panel", "Satellite", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight", "Lantern", "Compass", "Map", "GPS", "Stopwatch", "Thermometer", "Barometer", "Compass", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Jet engine", "Propeller", "Wind turbine", "Solar panel", "Satellite", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight", "Lantern", "Compass", "Map", "GPS", "Stopwatch", "Thermometer", "Barometer", "Compass", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Hot air balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Jet engine", "Propeller", "Wind turbine", "Solar panel", "Satellite", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight", "Lantern", "Compass", "Map", "GPS", "Stopwatch", "Thermometer", "Barometer", "Compass", "Telescope", "Microscope", "Binoculars", "Camera lens", "Tripod", "Flashlight"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/car196_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Helicopter", "Hot air balloon", "Jet", "Spaceship- Skateboard", "Roller skates", "Surfboard", "Snowboard", "Ski", "Scooter", "Segway", "Tricycle", "Wheelchair", "Stroller", "Shopping cart", "Golf cart", "Motorcycle helmet", "Bicycle helmet", "Jet pack", "Parachute", "Hang glider", "Paraglider", "Kite", "Balloon", "Zeppelin", "Blimp", "Drone", "Spacesuit", "Astronaut helmet", "Diving suit", "Life jacket", "Wetsuit", "Tuxedo", "Wedding dress", "Graduation gown", "Chef hat", "Cowboy hat", "Sombrero", "Baseball cap", "Beret", "Bowler hat", "Top hat", "Sun hat", "Headband", "Sunglasses", "Safety goggles", "Ski goggles", "Monocle", "Binoculars", "Telescope", "Microscope", "Magnifying glass", "Compass", "Stopwatch- Umbrella", "Briefcase", "Backpack", "Suitcase", "Wallet", "Purse", "Watch", "Bracelet", "Necklace", "Earrings", "Ring", "Sunglasses case", "Belt", "Tie", "Scarf", "Gloves", "Socks", "Shoes", "Sandals", "Boots", "High heels", "Sneakers", "Slippers", "Dress shirt", "T-shirt", "Blouse", "Sweater", "Jacket", "Coat", "Pants", "Jeans", "Shorts", "Skirt", "Dress", "Bathing suit", "Pajamas", "Robe", "Towel", "Blanket", "Pillow", "Bed", "Chair", "Table", "Desk", "Lamp", "Mirror", "Clock", "Vase", "Plant- Painting", "Sculpture", "Statue", "Pottery", "Tapestry", "Mosaic", "Photography", "Drawing", "Sketch", "Collage", "Print", "Calligraphy", "Graffiti", "Installation", "Performance art", "Film", "Theater", "Dance", "Music", "Concert", "Orchestra", "Choir", "Opera", "Ballet", "Comedy", "Drama", "Tragedy", "Poetry", "Novel", "Short story", "Biography", "Memoir", "Essay", "Magazine", "Newspaper", "Comic book", "Graphic novel", "Cookbook", "Self-help book", "Textbook", "Dictionary", "Encyclopedia", "Map", "Globe", "Puzzle", "Board game", "Card game", "Video game", "Virtual reality", "Augmented reality", "Podcast- Flower bouquet", "Fruit basket", "Vegetable garden", "Herb garden", "Cactus garden", "Bonsai tree", "Forest landscape", "Beach scene", "Mountain range", "Desert landscape", "City skyline", "Rural countryside", "Waterfall scenery", "Sunset view", "Sunrise view", "Starry night", "Cloudy sky", "Rainy day", "Snowy landscape", "Autumn foliage", "Spring blossoms", "Summer meadow", "Winter wonderland", "Ocean waves", "River stream", "Lake reflection", "Farm animals", "Zoo animals", "Safari wildlife", "Pet dog", "Pet cat", "Pet bird", "Pet fish", "Pet rabbit", "Wild horse", "Wild tiger", "Wild lion", "Wild bear", "Exotic reptile", "Underwater creatures", "Insects and bugs", "Butterflies and moths", "Birds of prey", "Tropical fish", "Marine mammals", "Forest creatures", "Farmyard animals", "Endangered species", "Mythical creatures- Abstract art", "Still life", "Landscape painting", "Portrait painting", "Impressionist art", "Cubist art", "Surrealist art", "Pop art", "Street art", "Contemporary art", "Minimalist art", "Expressionist art", "Realistic art", "Symbolist art", "Dada art", "Installation art", "Performance art", "Digital art", "Mixed media", "Collage art", "Sculptural art", "Ceramic art", "Glass art", "Textile art", "Metal art", "Wood art", "Paper art", "Fiber art", "Printmaking art", "Photography art", "Film art", "Animation art", "Documentary film", "Experimental film", "Action film", "Comedy film", "Drama film", "Fantasy film", "Horror film", "Musical film", "Romance film", "Science fiction film", "Thriller film", "Western film", "Adventure novel", "Crime novel", "Fantasy novel", "Historical novel", "Mystery novel", "Romance novel", "Science fiction novel"]} -------------------------------------------------------------------------------- /utils/imagenet_templates.py: -------------------------------------------------------------------------------- 1 | openai_imagenet_template = [ 2 | lambda c: f'a bad photo of a {c}.', 3 | lambda c: f'a photo of many {c}.', 4 | lambda c: f'a sculpture of a {c}.', 5 | lambda c: f'a photo of the hard to see {c}.', 6 | lambda c: f'a low resolution photo of the {c}.', 7 | lambda c: f'a rendering of a {c}.', 8 | lambda c: f'graffiti of a {c}.', 9 | lambda c: f'a bad photo of the {c}.', 10 | lambda c: f'a cropped photo of the {c}.', 11 | lambda c: f'a tattoo of a {c}.', 12 | lambda c: f'the embroidered {c}.', 13 | lambda c: f'a photo of a hard to see {c}.', 14 | lambda c: f'a bright photo of a {c}.', 15 | lambda c: f'a photo of a clean {c}.', 16 | lambda c: f'a photo of a dirty {c}.', 17 | lambda c: f'a dark photo of the {c}.', 18 | lambda c: f'a drawing of a {c}.', 19 | lambda c: f'a photo of my {c}.', 20 | lambda c: f'the plastic {c}.', 21 | lambda c: f'a photo of the cool {c}.', 22 | lambda c: f'a close-up photo of a {c}.', 23 | lambda c: f'a black and white photo of the {c}.', 24 | lambda c: f'a painting of the {c}.', 25 | lambda c: f'a painting of a {c}.', 26 | lambda c: f'a pixelated photo of the {c}.', 27 | lambda c: f'a sculpture of the {c}.', 28 | lambda c: f'a bright photo of the {c}.', 29 | lambda c: f'a cropped photo of a {c}.', 30 | lambda c: f'a plastic {c}.', 31 | lambda c: f'a photo of the dirty {c}.', 32 | lambda c: f'a jpeg corrupted photo of a {c}.', 33 | lambda c: f'a blurry photo of the {c}.', 34 | lambda c: f'a photo of the {c}.', 35 | lambda c: f'a good photo of the {c}.', 36 | lambda c: f'a rendering of the {c}.', 37 | lambda c: f'a {c} in a video game.', 38 | lambda c: f'a photo of one {c}.', 39 | lambda c: f'a doodle of a {c}.', 40 | lambda c: f'a close-up photo of the {c}.', 41 | lambda c: f'a photo of a {c}.', 42 | lambda c: f'the origami {c}.', 43 | lambda c: f'the {c} in a video game.', 44 | lambda c: f'a sketch of a {c}.', 45 | lambda c: f'a doodle of the {c}.', 46 | lambda c: f'a origami {c}.', 47 | lambda c: f'a low resolution photo of a {c}.', 48 | lambda c: f'the toy {c}.', 49 | lambda c: f'a rendition of the {c}.', 50 | lambda c: f'a photo of the clean {c}.', 51 | lambda c: f'a photo of a large {c}.', 52 | lambda c: f'a rendition of a {c}.', 53 | lambda c: f'a photo of a nice {c}.', 54 | lambda c: f'a photo of a weird {c}.', 55 | lambda c: f'a blurry photo of a {c}.', 56 | lambda c: f'a cartoon {c}.', 57 | lambda c: f'art of a {c}.', 58 | lambda c: f'a sketch of the {c}.', 59 | lambda c: f'a embroidered {c}.', 60 | lambda c: f'a pixelated photo of a {c}.', 61 | lambda c: f'itap of the {c}.', 62 | lambda c: f'a jpeg corrupted photo of the {c}.', 63 | lambda c: f'a good photo of a {c}.', 64 | lambda c: f'a plushie {c}.', 65 | lambda c: f'a photo of the nice {c}.', 66 | lambda c: f'a photo of the small {c}.', 67 | lambda c: f'a photo of the weird {c}.', 68 | lambda c: f'the cartoon {c}.', 69 | lambda c: f'art of the {c}.', 70 | lambda c: f'a drawing of the {c}.', 71 | lambda c: f'a photo of the large {c}.', 72 | lambda c: f'a black and white photo of a {c}.', 73 | lambda c: f'the plushie {c}.', 74 | lambda c: f'a dark photo of a {c}.', 75 | lambda c: f'itap of a {c}.', 76 | lambda c: f'graffiti of the {c}.', 77 | lambda c: f'a toy {c}.', 78 | lambda c: f'itap of my {c}.', 79 | lambda c: f'a photo of a cool {c}.', 80 | lambda c: f'a photo of a small {c}.', 81 | lambda c: f'a tattoo of the {c}.', 82 | ] 83 | 84 | 85 | openai_imagenet_template_subset = { 86 | 0: [ 87 | lambda c: f'a photo of a {c}.', 88 | lambda c: f'a blurry photo of a {c}.', 89 | lambda c: f'a photo of many {c}.', 90 | lambda c: f'a photo of the large {c}.', 91 | lambda c: f'a photo of the small {c}.', 92 | ], 93 | 1: [ 94 | lambda c: f'itap of my {c}.', 95 | lambda c: f'a bad photo of a {c}.', 96 | lambda c: f'a origami {c}.', 97 | lambda c: f'a photo of the large {c}.', 98 | lambda c: f'a {c} in a video game.', 99 | lambda c: f'art of the {c}.', 100 | lambda c: f'a photo of the small {c}.', 101 | ] 102 | } -------------------------------------------------------------------------------- /envisioned_classes/far_300/ImageNet_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Sunset", "Rainbow", "Snowflake", "Lightning", "Aurora", "Galaxy", "Moon", "Star", "Cloud", "Forest", "Mountain range", "Beach", "Canyon", "Glacier", "Island", "Jungle", "Lake", "River", "Ocean", "Volcano", "Waterfall", "Desert", "Sunset", "Rainbow", "Snowflake", "Lightning", "Aurora", "Galaxy", "Moon", "Star", "Cloud", "Forest", "Mountain range", "Beach", "Canyon", "Glacier", "Island", "Jungle", "Lake", "River", "Ocean", "Volcano", "Waterfall", "Desert", "Sunset", "Rainbow- Meadow", "Sunrise", "Tornado", "Lightning bolt", "Comet", "Nebula", "Star cluster", "Sand dunes", "Iceberg", "Rainforest", "Waterfall", "Canyon", "Oasis", "Savannah", "Coral reef", "Tundra", "Archipelago", "Mangrove forest", "Geyser", "Lagoon", "Fjord", "Cavern", "Plateau", "Steppe", "Dune", "Bluff", "Marshland", "Bog", "Estuary", "Rapids", "Waterfall", "Oasis", "Savannah", "Coral reef", "Tundra", "Archipelago", "Mangrove forest", "Geyser", "Lagoon", "Fjord", "Cavern", "Plateau", "Steppe", "Dune", "Bluff", "Marshland", "Bog", "Estuary", "Rapids- Wheat field", "Thunderstorm", "Avalanche", "Shooting star", "Solar system", "Meteor shower", "Ice cave", "Sandstorm", "Bamboo forest", "Geothermal pool", "Coral atoll", "Arctic tundra", "Arch formation", "Mangrove swamp", "Mud volcano", "Salt flat", "Prairie grassland", "Sinkhole", "Oasis lake", "Glacial lake", "Underground river", "Mesa formation", "Steppe grassland", "Sandstone cliff", "Fen wetland", "Bog ecosystem", "Salt marsh", "Waterfall cascade", "Oasis palm trees", "Savannah wildlife", "Coral polyps", "Tundra wildlife", "Archipelago islands", "Mangrove roots", "Geyser eruption", "Lagoon paradise", "Fjord coastline", "Cavern stalactites", "Plateau landscape", "Steppe horizon", "Dune formation", "Bluff overlook", "Marsh reeds", "Bog moss", "Estuary birds", "Rapids whitewater", "Wheat field", "Thunderstorm", "Avalanche", "Shooting star", "Solar system", "Meteor shower", "Ice cave", "Sandstorm- Cherry blossom", "Autumn foliage", "Rainbow waterfall", "Desert oasis", "Lightning storm", "Comet tail", "Starry night", "Sand dune", "Ice formation", "Rainforest canopy", "Canyon walls", "Oasis palm trees", "Savannah grassland", "Coral reef fish", "Tundra wildlife", "Archipelago islands", "Mangrove roots", "Geyser eruption", "Lagoon paradise", "Fjord coastline", "Cavern stalactites", "Plateau landscape", "Steppe horizon", "Dune formation", "Bluff overlook", "Marsh reeds", "Bog moss", "Estuary birds", "Rapids whitewater", "Wheat field", "Thunderstorm", "Avalanche", "Shooting star", "Solar system", "Meteor shower", "Ice cave", "Sandstorm", "Bamboo forest", "Geothermal pool", "Coral atoll", "Arctic tundra", "Arch formation", "Mangrove swamp", "Mud volcano", "Salt flat", "Prairie grassland", "Sinkhole", "Oasis lake", "Glacial lake", "Underground river- Cherry blossom", "Autumn foliage", "Rainbow waterfall", "Desert oasis", "Lightning storm", "Comet tail", "Starry night", "Sand dune", "Ice formation", "Rainforest canopy", "Canyon walls", "Oasis palm trees", "Savannah grassland", "Coral reef fish", "Tundra wildlife", "Archipelago islands", "Mangrove roots", "Geyser eruption", "Lagoon paradise", "Fjord coastline", "Cavern stalactites", "Plateau landscape", "Steppe horizon", "Dune formation", "Bluff overlook", "Marsh reeds", "Bog moss", "Estuary birds", "Rapids whitewater", "Wheat field", "Thunderstorm", "Avalanche", "Shooting star", "Solar system", "Meteor shower", "Ice cave", "Sandstorm", "Bamboo forest", "Geothermal pool", "Coral atoll", "Arctic tundra", "Arch formation", "Mangrove swamp", "Mud volcano", "Salt flat", "Prairie grassland", "Sinkhole", "Oasis lake", "Glacial lake", "Underground river- Cherry blossom", "Autumn foliage", "Rainbow waterfall", "Desert oasis", "Lightning storm", "Comet tail", "Starry night", "Sand dune", "Ice formation", "Rainforest canopy", "Canyon walls", "Oasis palm trees", "Savannah grassland", "Coral reef fish", "Tundra wildlife", "Archipelago islands", "Mangrove roots", "Geyser eruption", "Lagoon paradise", "Fjord coastline", "Cavern stalactites", "Plateau landscape", "Steppe horizon", "Dune formation", "Bluff overlook", "Marsh reeds", "Bog moss", "Estuary birds", "Rapids whitewater", "Wheat field", "Thunderstorm", "Avalanche", "Shooting star", "Solar system", "Meteor shower", "Ice cave", "Sandstorm", "Bamboo forest", "Geothermal pool", "Coral atoll", "Arctic tundra", "Arch formation", "Mangrove swamp", "Mud volcano", "Salt flat", "Prairie grassland", "Sinkhole", "Oasis lake", "Glacial lake", "Underground river"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/bird200_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Desert", "Snowy mountain", "Beach", "Sunset", "Forest", "City skyline", "Lake", "Canyon", "Volcano", "Glacier", "Coral reef", "Wheat field", "Vineyard", "Rainforest", "Savannah", "Deserted island", "Lighthouse", "Hot air balloon", "Aurora borealis", "Castle", "Ruins", "Windmill", "Pagoda", "Taj Mahal", "Eiffel Tower", "Statue of Liberty", "Great Wall of China", "Machu Picchu", "Pyramids of Giza", "Colosseum", "Sydney Opera House", "Mount Rushmore", "Stonehenge", "Angkor Wat", "Santorini", "Venice canals", "Grand Canyon", "Niagara Falls", "Mount Everest", "Serengeti", "Amazon rainforest", "Great Barrier Reef", "Sahara Desert", "Himalayas", "Rocky Mountains", "Alps", "Andes Mountains- Tulip field", "Bamboo forest", "Cherry blossoms", "Lavender field", "Palm trees", "Cactus garden", "Redwood forest", "Autumn leaves", "Snowy forest", "Rolling hills", "Wheat harvest", "Sunflower field", "Lotus pond", "Bamboo grove", "Mossy forest", "Cherry orchard", "Poppy field", "Maple trees", "Pine forest", "Moss-covered rocks", "Mossy waterfall", "Bamboo bridge", "Mossy ruins", "Mossy tree trunk", "Mossy cave", "Mossy path", "Mossy steps", "Mossy wall", "Mossy roof", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy fence", "Mossy gate", "Mossy window", "Mossy door", "Mossy staircase", "Mossy well", "Mossy pond", "Mossy riverbank", "Mossy bridge", "Mossy gazebo", "Mossy tower", "Mossy castle", "Mossy cottage", "Mossy hut", "Mossy barn", "Mossy shed", "Mossy shed", "Mossy shed- Cherry blossom", "Bamboo forest", "Lavender field", "Palm trees", "Cactus garden", "Redwood forest", "Autumn leaves", "Snowy forest", "Rolling hills", "Wheat harvest", "Sunflower field", "Lotus pond", "Bamboo grove", "Mossy forest", "Cherry orchard", "Poppy field", "Maple trees", "Pine forest", "Moss-covered rocks", "Mossy waterfall", "Bamboo bridge", "Mossy ruins", "Mossy tree trunk", "Mossy cave", "Mossy path", "Mossy steps", "Mossy wall", "Mossy roof", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy fence", "Mossy gate", "Mossy window", "Mossy door", "Mossy staircase", "Mossy well", "Mossy pond", "Mossy riverbank", "Mossy bridge", "Mossy gazebo", "Mossy tower", "Mossy castle", "Mossy cottage", "Mossy hut", "Mossy barn", "Mossy shed", "Mossy shed", "Mossy shed- Cherry blossom", "Bamboo forest", "Lavender field", "Palm trees", "Cactus garden", "Redwood forest", "Autumn leaves", "Snowy forest", "Rolling hills", "Wheat harvest", "Sunflower field", "Lotus pond", "Bamboo grove", "Mossy forest", "Cherry orchard", "Poppy field", "Maple trees", "Pine forest", "Moss-covered rocks", "Mossy waterfall", "Bamboo bridge", "Mossy ruins", "Mossy tree trunk", "Mossy cave", "Mossy path", "Mossy steps", "Mossy wall", "Mossy roof", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy fence", "Mossy gate", "Mossy window", "Mossy door", "Mossy staircase", "Mossy well", "Mossy pond", "Mossy riverbank", "Mossy bridge", "Mossy gazebo", "Mossy tower", "Mossy castle", "Mossy cottage", "Mossy hut", "Mossy barn", "Mossy shed", "Mossy shed", "Mossy shed- Cherry blossom", "Bamboo forest", "Lavender field", "Palm trees", "Cactus garden", "Redwood forest", "Autumn leaves", "Snowy forest", "Rolling hills", "Wheat harvest", "Sunflower field", "Lotus pond", "Bamboo grove", "Mossy forest", "Cherry orchard", "Poppy field", "Maple trees", "Pine forest", "Moss-covered rocks", "Mossy waterfall", "Bamboo bridge", "Mossy ruins", "Mossy tree trunk", "Mossy cave", "Mossy path", "Mossy steps", "Mossy wall", "Mossy roof", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy fence", "Mossy gate", "Mossy window", "Mossy door", "Mossy staircase", "Mossy well", "Mossy pond", "Mossy riverbank", "Mossy bridge", "Mossy gazebo", "Mossy tower", "Mossy castle", "Mossy cottage", "Mossy hut", "Mossy barn", "Mossy shed", "Mossy shed", "Mossy shed- Cherry blossom", "Bamboo forest", "Lavender field", "Palm trees", "Cactus garden", "Redwood forest", "Autumn leaves", "Snowy forest", "Rolling hills", "Wheat harvest", "Sunflower field", "Lotus pond", "Bamboo grove", "Mossy forest", "Cherry orchard", "Poppy field", "Maple trees", "Pine forest", "Moss-covered rocks", "Mossy waterfall", "Bamboo bridge", "Mossy ruins", "Mossy tree trunk", "Mossy cave", "Mossy path", "Mossy steps", "Mossy wall", "Mossy roof", "Mossy gate", "Mossy statue", "Mossy bench", "Mossy fence", "Mossy gate", "Mossy window", "Mossy door", "Mossy staircase", "Mossy well", "Mossy pond", "Mossy riverbank", "Mossy bridge", "Mossy gazebo", "Mossy tower", "Mossy castle", "Mossy cottage", "Mossy hut", "Mossy barn", "Mossy shed", "Mossy shed", "Mossy shed"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar100_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"apple": ["tomato", "cherry", "strawberry"], "aquarium_fish": ["butterflies", "flowers", "birds"], "baby": ["kitten", "teddy bear", "flower bouquet"], "bear": ["gorilla", "panda", "koala"], "beaver": ["otter", "capybara", "platypus"], "bed": ["couch", "hammock", "tent"], "bee": ["wasp", "ladybug", "butterfly"], "beetle": ["ladybug", "scarab", "stag beetle"], "bicycle": ["motorcycle", "skateboard", "shopping cart"], "bottle": ["vase", "flask", "canister"], "bowl": ["vase", "helmet", "pot"], "boy": ["man", "toddler", "teenager"], "bridge": ["viaduct", "aqueduct", "pier"], "bus": ["ambulance", "tractor", "hot air balloon"], "butterfly": ["flower", "stained glass window", "peacock"], "camel": ["giraffe", "kangaroo", "elephant"], "can": ["tin can", "soda can", "aluminum can"], "castle": ["lighthouse", "palace", "windmill"], "caterpillar": ["earthworm", "centipede", "millipede"], "cattle": ["horses", "deer", "elephants"], "chair": ["stool", "bench", "ladder"], "chimpanzee": ["orangutan", "gorilla", "baboon"], "clock": ["compass", "hourglass", "sundial"], "cloud": ["cotton candy", "marshmallow", "foam"], "cockroach": ["scorpion", "centipede", "spider"], "couch": ["armchair", "ottoman", "chaise lounge"], "crab": ["scorpion", "lobster", "spider"], "crocodile": ["alligator", "monitor lizard", "komodo dragon"], "cup": ["vase", "bowl", "teapot"], "dinosaur": ["dragon", "crocodile", "lizard"], "dolphin": ["killer whale", "sailfish", "sea turtle"], "elephant": ["rhinoceros", "giraffe", "hippopotamus"], "flatfish": ["stingray", "butterfly", "leaf"], "forest": ["jungle", "mountain range", "coral reef"], "fox": ["red panda", "squirrel", "fennec fox"], "girl": ["woman", "child", "bride"], "hamster": ["guinea pig", "squirrel", "kangaroo"], "house": ["castle", "lighthouse", "igloo"], "kangaroo": ["wallaby", "wombat", "meerkat"], "keyboard": ["typewriter", "calculator", "piano"], "lamp": ["candle", "flashlight", "lantern"], "lawn_mower": ["tractor", "chainsaw", "leaf blower"], "leopard": ["cheetah", "jaguar", "ocelot"], "lion": ["tiger", "cheetah", "leopard"], "lizard": ["snake", "turtle", "crocodile"], "lobster": ["scorpion", "crab", "mantis shrimp"], "man": ["businessman", "firefighter", "astronaut"], "maple_tree": ["oak tree", "palm tree", "pine tree"], "motorcycle": ["bicycle", "scooter", "skateboard"], "mountain": ["skyscrapers", "sand dunes", "ocean waves"], "mouse": ["squirrel", "remote control", "hamster"], "mushroom": ["coral", "tree bark", "sea sponge"], "oak_tree": ["pine tree", "palm tree", "maple tree"], "orange": ["pumpkin", "tangerine", "sunset"], "orchid": ["tulip", "sunflower", "daisy"], "otter": ["beaver", "seal", "platypus"], "palm_tree": ["cactus", "fern", "bamboo"], "pear": ["avocado", "bell pepper", "kiwi"], "pickup_truck": ["bulldozer", "sailboat", "hot air balloon"], "pine_tree": ["cactus", "palm tree", "fern"], "plain": ["desert", "grassland", "ocean"], "plate": ["frisbee", "CD", "pizza"], "poppy": ["sunflower", "tulip", "daisy"], "porcupine": ["hedgehog", "cactus", "sea urchin"], "possum": ["raccoon", "koala", "sloth"], "rabbit": ["kangaroo", "squirrel", "chinchilla"], "raccoon": ["red panda", "squirrel", "meerkat"], "ray": ["stingray", "manta ray", "eagle ray"], "road": ["river", "railway", "runway"], "rocket": ["fireworks", "lighthouse", "airplane"], "rose": ["tulip", "sunflower", "daisy"], "sea": ["sky", "desert", "forest"], "seal": ["walrus", "sea lion", "otter"], "shark": ["swordfish", "crocodile", "scorpion"], "shrew": ["mouse", "mole", "hedgehog"], "skunk": ["panda", "zebra", "badger"], "skyscraper": ["lighthouse", "windmill", "pagoda"], "snail": ["seashell", "caterpillar", "turtle"], "snake": ["earthworm", "eel", "lizard"], "spider": ["scorpion", "lobster", "centipede"], "squirrel": ["chipmunk", "kangaroo", "meerkat"], "streetcar": ["roller coaster", "cable car", "gondola"], "sunflower": ["dandelion", "pineapple", "marigold"], "sweet_pepper": ["chili pepper", "tomato", "pumpkin"], "table": ["chair", "desk", "bed"], "tank": ["submarine", "bulldozer", "spaceship"], "telephone": ["microphone", "typewriter", "radio"], "television": ["computer monitor", "microwave", "washing machine"], "tiger": ["leopard", "lion", "cheetah"], "tractor": ["bulldozer", "combine harvester", "forklift"], "train": ["roller coaster", "subway", "cruise ship"], "trout": ["flamingo", "peacock", "butterfly"], "tulip": ["sunflower", "daisy", "rose"], "turtle": ["tortoise", "seahorse", "armadillo"], "wardrobe": ["filing cabinet", "bookshelf", "refrigerator"], "whale": ["dolphin", "submarine", "seagull"], "willow_tree": ["palm tree", "fern", "bamboo"], "wolf": ["husky dog", "gray fox", "coyote"], "woman": ["man", "child", "elderly person"], "worm": ["snake", "caterpillar", "earthworm"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar100_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"apple": ["tomato", "cherry", "strawberry"], "aquarium_fish": ["butterflies", "flowers", "birds"], "baby": ["kitten", "teddy bear", "flower bouquet"], "bear": ["gorilla", "panda", "koala"], "beaver": ["otter", "capybara", "platypus"], "bed": ["couch", "hammock", "tent"], "bee": ["wasp", "ladybug", "butterfly"], "beetle": ["ladybug", "scarab", "stag beetle"], "bicycle": ["motorcycle", "skateboard", "shopping cart"], "bottle": ["vase", "flask", "canister"], "bowl": ["vase", "helmet", "pot"], "boy": ["man", "toddler", "elderly person"], "bridge": ["viaduct", "aqueduct", "pier"], "bus": ["ambulance", "tractor", "hot air balloon"], "butterfly": ["flower", "stained glass window", "peacock"], "camel": ["giraffe", "kangaroo", "elephant"], "can": ["tin", "bottle", "jar"], "castle": ["lighthouse", "palace", "temple"], "caterpillar": ["earthworm", "centipede", "millipede"], "cattle": ["horses", "deer", "elephants"], "chair": ["stool", "bench", "ladder"], "chimpanzee": ["orangutan", "gorilla", "baboon"], "clock": ["compass", "hourglass", "sundial"], "cloud": ["cotton candy", "marshmallow", "foam"], "cockroach": ["scorpion", "lobster", "centipede"], "couch": ["armchair", "ottoman", "chaise lounge"], "crab": ["scorpion", "lobster", "spider"], "crocodile": ["alligator", "monitor lizard", "komodo dragon"], "cup": ["vase", "bowl", "teapot"], "dinosaur": ["dragon", "crocodile", "lizard"], "dolphin": ["killer whale", "sailfish", "sea turtle"], "elephant": ["rhinoceros", "giraffe", "hippopotamus"], "flatfish": ["stingray", "butterfly", "leaf"], "forest": ["jungle", "mountain range", "coral reef"], "fox": ["red panda", "squirrel", "fennec fox"], "girl": ["woman", "child", "bride"], "hamster": ["squirrel", "guinea pig", "kangaroo"], "house": ["castle", "lighthouse", "igloo"], "kangaroo": ["wallaby", "wombat", "meerkat"], "keyboard": ["typewriter", "piano", "calculator"], "lamp": ["candle", "flashlight", "lantern"], "lawn_mower": ["tractor", "chainsaw", "leaf blower"], "leopard": ["cheetah", "jaguar", "ocelot"], "lion": ["tiger", "cheetah", "leopard"], "lizard": ["snake", "turtle", "crocodile"], "lobster": ["scorpion", "crab", "mantis shrimp"], "man": ["businessman", "firefighter", "astronaut"], "maple_tree": ["oak tree", "palm tree", "pine tree"], "motorcycle": ["bicycle", "scooter", "skateboard"], "mountain": ["skyscrapers", "sand dunes", "ocean waves"], "mouse": ["squirrel", "remote control", "hamster"], "mushroom": ["coral", "tree bark", "sea sponge"], "oak_tree": ["pine tree", "palm tree", "maple tree"], "orange": ["pumpkin", "tangerine", "sunset"], "orchid": ["tulip", "sunflower", "daisy"], "otter": ["beaver", "seal", "platypus"], "palm_tree": ["cactus", "fern", "bamboo"], "pear": ["avocado", "bell pepper", "kiwi"], "pickup_truck": ["construction crane", "tractor", "dump truck"], "pine_tree": ["cactus", "palm tree", "fern"], "plain": ["desert", "grassland", "ocean"], "plate": ["frisbee", "CD", "steering wheel"], "poppy": ["sunflower", "tulip", "daisy"], "porcupine": ["hedgehog", "cactus", "sea urchin"], "possum": ["raccoon", "koala", "sloth"], "rabbit": ["kangaroo", "squirrel", "chinchilla"], "raccoon": ["red panda", "squirrel", "meerkat"], "ray": ["stingray", "manta ray", "eagle ray"], "road": ["river", "railway", "runway"], "rocket": ["fireworks", "lighthouse", "airplane"], "rose": ["tulip", "sunflower", "daisy"], "sea": ["sky", "desert", "forest"], "seal": ["walrus", "sea lion", "otter"], "shark": ["swordfish", "crocodile", "scorpion"], "shrew": ["mouse", "mole", "hedgehog"], "skunk": ["panda", "zebra", "badger"], "skyscraper": ["lighthouse", "windmill", "pagoda"], "snail": ["seashell", "caterpillar", "turtle"], "snake": ["earthworm", "eel", "lizard"], "spider": ["scorpion", "lobster", "centipede"], "squirrel": ["chipmunk", "kangaroo", "meerkat"], "streetcar": ["roller coaster", "cable car", "gondola"], "sunflower": ["dandelion", "pineapple", "marigold"], "sweet_pepper": ["chili pepper", "tomato", "pumpkin"], "table": ["chair", "desk", "bed"], "tank": ["submarine", "bulldozer", "spaceship"], "telephone": ["microphone", "typewriter", "radio"], "television": ["computer monitor", "microwave", "washing machine"], "tiger": ["leopard", "lion", "cheetah"], "tractor": ["bulldozer", "combine harvester", "forklift"], "train": ["roller coaster", "subway", "cruise ship"], "trout": ["flamingo", "peacock", "toucan"], "tulip": ["sunflower", "daisy", "rose"], "turtle": ["tortoise", "seahorse", "armadillo"], "wardrobe": ["filing cabinet", "bookshelf", "refrigerator"], "whale": ["dolphin", "submarine", "seagull"], "willow_tree": ["palm tree", "fern", "bamboo"], "wolf": ["husky dog", "gray fox", "coyote"], "woman": ["man", "child", "elderly person"], "worm": ["snake", "caterpillar", "earthworm"]} -------------------------------------------------------------------------------- /envisioned_classes/near_3/cifar100_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"apple": ["tomato", "cherry", "strawberry"], "aquarium_fish": ["butterflies", "flowers", "birds"], "baby": ["kitten", "teddy bear", "flower bouquet"], "bear": ["gorilla", "panda", "koala"], "beaver": ["otter", "capybara", "platypus"], "bed": ["couch", "hammock", "tent"], "bee": ["wasp", "ladybug", "butterfly"], "beetle": ["ladybug", "scarab", "stag beetle"], "bicycle": ["motorcycle", "skateboard", "shopping cart"], "bottle": ["vase", "flask", "canister"], "bowl": ["vase", "helmet", "pot"], "boy": ["man", "toddler", "elderly person"], "bridge": ["viaduct", "aqueduct", "pier"], "bus": ["ambulance", "tractor", "hot air balloon"], "butterfly": ["flower", "stained glass window", "peacock"], "camel": ["giraffe", "kangaroo", "elephant"], "can": ["tin can", "soda can", "aluminum can"], "castle": ["lighthouse", "palace", "temple"], "caterpillar": ["earthworm", "centipede", "millipede"], "cattle": ["horses", "deer", "elephants"], "chair": ["stool", "bench", "ladder"], "chimpanzee": ["orangutan", "gorilla", "baboon"], "clock": ["compass", "hourglass", "sundial"], "cloud": ["cotton candy", "marshmallow", "foam"], "cockroach": ["scorpion", "lobster", "centipede"], "couch": ["armchair", "ottoman", "chaise lounge"], "crab": ["scorpion", "lobster", "spider"], "crocodile": ["alligator", "monitor lizard", "komodo dragon"], "cup": ["vase", "bowl", "teapot"], "dinosaur": ["dragon", "crocodile", "lizard"], "dolphin": ["killer whale", "sailfish", "sea turtle"], "elephant": ["rhinoceros", "giraffe", "hippopotamus"], "flatfish": ["stingray", "butterfly", "leaf"], "forest": ["jungle", "mountain range", "coral reef"], "fox": ["red panda", "squirrel", "fennec fox"], "girl": ["woman", "child", "bride"], "hamster": ["squirrel", "guinea pig", "kangaroo"], "house": ["skyscraper", "lighthouse", "igloo"], "kangaroo": ["wallaby", "wombat", "meerkat"], "keyboard": ["typewriter", "calculator", "piano"], "lamp": ["candle", "flashlight", "lantern"], "lawn_mower": ["tractor", "chainsaw", "leaf blower"], "leopard": ["cheetah", "jaguar", "ocelot"], "lion": ["tiger", "cheetah", "leopard"], "lizard": ["snake", "turtle", "crocodile"], "lobster": ["scorpion", "crab", "mantis shrimp"], "man": ["businessman", "firefighter", "astronaut"], "maple_tree": ["oak tree", "palm tree", "pine tree"], "motorcycle": ["bicycle", "scooter", "skateboard"], "mountain": ["skyscrapers", "sand dunes", "ocean waves"], "mouse": ["squirrel", "remote control", "hamster"], "mushroom": ["coral", "tree bark", "sea sponge"], "oak_tree": ["pine tree", "palm tree", "maple tree"], "orange": ["pumpkin", "tangerine", "sunset"], "orchid": ["tulip", "sunflower", "daisy"], "otter": ["beaver", "seal", "platypus"], "palm_tree": ["cactus", "fern", "bamboo"], "pear": ["avocado", "bell pepper", "kiwi"], "pickup_truck": ["construction crane", "tractor", "dump truck"], "pine_tree": ["cactus", "palm tree", "fern"], "plain": ["desert", "grassland", "ocean"], "plate": ["frisbee", "CD", "pizza"], "poppy": ["sunflower", "tulip", "daisy"], "porcupine": ["hedgehog", "cactus", "sea urchin"], "possum": ["raccoon", "koala", "sloth"], "rabbit": ["kangaroo", "squirrel", "chinchilla"], "raccoon": ["red panda", "squirrel", "meerkat"], "ray": ["stingray", "manta ray", "eagle ray"], "road": ["river", "railway", "runway"], "rocket": ["fireworks", "lighthouse", "airplane"], "rose": ["tulip", "sunflower", "daisy"], "sea": ["sky", "desert", "forest"], "seal": ["walrus", "sea lion", "otter"], "shark": ["swordfish", "crocodile", "scorpion"], "shrew": ["mouse", "mole", "hedgehog"], "skunk": ["panda", "zebra", "badger"], "skyscraper": ["lighthouse", "windmill", "pagoda"], "snail": ["seashell", "caterpillar", "turtle"], "snake": ["earthworm", "eel", "lizard"], "spider": ["scorpion", "lobster", "centipede"], "squirrel": ["chipmunk", "kangaroo", "meerkat"], "streetcar": ["roller coaster", "cable car", "gondola"], "sunflower": ["dandelion", "pineapple", "marigold"], "sweet_pepper": ["chili pepper", "tomato", "pumpkin"], "table": ["chair", "desk", "bed"], "tank": ["submarine", "bulldozer", "spaceship"], "telephone": ["microphone", "typewriter", "radio"], "television": ["computer monitor", "microwave", "washing machine"], "tiger": ["leopard", "lion", "cheetah"], "tractor": ["bulldozer", "combine harvester", "forklift"], "train": ["roller coaster", "subway", "cruise ship"], "trout": ["flamingo", "peacock", "butterfly"], "tulip": ["sunflower", "daisy", "rose"], "turtle": ["tortoise", "seahorse", "armadillo"], "wardrobe": ["filing cabinet", "bookshelf", "refrigerator"], "whale": ["dolphin", "submarine", "seagull"], "willow_tree": ["palm tree", "fern", "bamboo"], "wolf": ["coyote", "hyena", "fox"], "woman": ["man", "child", "elderly person"], "worm": ["snake", "caterpillar", "earthworm"]} -------------------------------------------------------------------------------- /envisioned_classes/far_500/pet37_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Tiger", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Dolphin", "Cherry tomatoes", "Fireworks", "Zebra", "Cherry cola", "Hot chocolate", "Dolphin sculpture", "Cherry blossom tree", "Firefighter", "Zebra print", "Cherry wood furniture", "Hot springs", "Dolphin tattoo", "Cherry blossom festival", "Firefighter helmet", "Zebra crossing", "Cherry blossom wallpaper", "Hot air balloon festival", "Dolphin necklace", "Cherry blossom painting", "Firefighter uniform", "Zebra cake", "Cherry blossom tattoo", "Hot air balloon ride", "Dolphin show", "Cherry blossom branch", "Firefighter truck- Sunset beach", "Snowy owl", "Bamboo forest", "Cherry blossom field", "Waterfall hike", "Mountain sunrise", "Desert sunset", "Peacock feathers", "Northern lights", "Deep sea creatures", "Sequoia tree", "Desert cactus", "Koala bear", "Sakura petals", "African elephant", "Double rainbow", "Coastal lighthouse", "Bengal tiger", "Fall foliage", "Emperor penguin", "Cherry pie slice", "Hot air balloon ride", "Bottlenose dolphin", "Cherry tomato salad", "Fourth of July fireworks", "Zebra stripes", "Cherry cola drink", "Hot chocolate mug", "Dolphin sculpture art", "Cherry blossom tree branch", "Firefighter equipment", "Zebra print fabric", "Cherry wood table", "Hot springs resort", "Dolphin tattoo design", "Cherry blossom festival parade", "Firefighter helmet safety", "Zebra crossing sign", "Cherry blossom wallpaper pattern", "Hot air balloon festival event", "Dolphin necklace pendant", "Cherry blossom painting art", "Firefighter uniform badge", "Zebra cake decoration", "Cherry blossom tattoo design", "Hot air balloon ride adventure", "Dolphin show performance", "Cherry blossom branch decoration", "Firefighter truck engine- Sunset over water", "Snowy landscape", "Bamboo forest path", "Cherry blossom garden", "Waterfall cascades", "Mountain peak view", "Desert dunes landscape", "Peacock display", "Aurora borealis sky", "Underwater coral reef", "Giant sequoia tree", "Desert plant life", "Cute koala bear", "Sakura cherry blossoms", "African safari animals", "Vibrant rainbow colors", "Coastal lighthouse scene", "Majestic tiger portrait", "Colorful autumn leaves", "Playful penguin colony", "Delicious cherry pie", "Hot air balloon festival", "Graceful dolphin leap", "Fresh cherry tomato", "Spectacular fireworks show", "Striking zebra pattern", "Refreshing cherry cola", "Warm hot chocolate", "Elegant dolphin sculpture", "Blossoming cherry tree", "Brave firefighter hero", "Stylish zebra print", "Solid cherry wood", "Relaxing hot springs", "Artistic dolphin tattoo", "Cherry blossom festival", "Protective firefighter gear", "Safe zebra crossing", "Beautiful cherry blossom", "Exciting hot air balloon", "Captivating dolphin show", "Delicate cherry blossom", "Brave firefighter truck", "Serene zebra grazing", "Exquisite cherry wood", "Soothing hot springs", "Unique dolphin necklace", "Graceful cherry blossom", "Daring hot air balloon", "Mesmerizing dolphin display", "Delightful cherry pie", "Courageous firefighter rescue", "Striped zebra mane", "Fragrant cherry blossom", "Thrilling hot air balloon- Beach sunset view", "Snowy mountain peak", "Bamboo forest trail", "Cherry blossom park", "Waterfall plunge pool", "Mountain range panorama", "Desert oasis scene", "Peacock feather pattern", "Northern lights display", "Colorful coral reef", "Towering sequoia tree", "Desert cactus garden", "Adorable koala bear", "Sakura cherry blossoms", "Majestic African elephant", "Vibrant double rainbow", "Coastal lighthouse beacon", "Striking Bengal tiger", "Golden autumn foliage", "Playful baby penguin", "Sweet cherry pie slice", "Hot air balloon adventure", "Graceful bottlenose dolphin", "Fresh cherry tomato salad", "Dazzling fireworks spectacle", "Zebra stripe pattern", "Cherry cola beverage", "Creamy hot chocolate", "Lifelike dolphin sculpture", "Blossoming cherry tree branch", "Brave firefighter team", "Stylish zebra print fabric", "Solid cherry wood furniture", "Relaxing hot springs resort", "Artistic dolphin tattoo design", "Cherry blossom festival parade", "Protective firefighter helmet", "Safe zebra crossing road", "Beautiful cherry blossom petals", "Exciting hot air balloon ride", "Captivating dolphin show performance", "Delicate cherry blossom flowers", "Brave firefighter uniform", "Serene zebra grazing field", "Exquisite cherry wood table", "Soothing hot springs water", "Unique dolphin necklace pendant", "Graceful cherry blossom branch", "Daring hot air balloon flight", "Mesmerizing dolphin swimming"]} -------------------------------------------------------------------------------- /utils/language_models.py: -------------------------------------------------------------------------------- 1 | import gc 2 | import os 3 | import time 4 | from typing import Dict, List 5 | 6 | import openai 7 | import torch 8 | 9 | 10 | class LanguageModel(): 11 | def __init__(self, model_name): 12 | self.model_name = model_name 13 | 14 | def batched_generate(self, prompts_list: List, max_n_tokens: int, temperature: float): 15 | """ 16 | Generates responses for a batch of prompts using a language model. 17 | """ 18 | raise NotImplementedError 19 | 20 | class HuggingFace(LanguageModel): 21 | def __init__(self,model_name, model, tokenizer): 22 | self.model_name = model_name 23 | self.model = model 24 | self.tokenizer = tokenizer 25 | self.eos_token_ids = [self.tokenizer.eos_token_id] 26 | 27 | def batched_generate(self, 28 | full_prompts_list, 29 | max_n_tokens: int, 30 | temperature: float, 31 | top_p: float = 1.0,): 32 | inputs = self.tokenizer(full_prompts_list, return_tensors='pt', padding=True) 33 | inputs = {k: v.to(self.model.device.index) for k, v in inputs.items()} 34 | 35 | # Batch generation 36 | if temperature > 0: 37 | output_ids = self.model.generate( 38 | **inputs, 39 | max_new_tokens=max_n_tokens, 40 | do_sample=True, 41 | temperature=temperature, 42 | eos_token_id=self.eos_token_ids, 43 | top_p=top_p, 44 | ) 45 | else: 46 | output_ids = self.model.generate( 47 | **inputs, 48 | max_new_tokens=max_n_tokens, 49 | do_sample=False, 50 | eos_token_id=self.eos_token_ids, 51 | top_p=1, 52 | temperature=1, # To prevent warning messages 53 | ) 54 | 55 | # If the model is not an encoder-decoder type, slice off the input tokens 56 | if not self.model.config.is_encoder_decoder: 57 | output_ids = output_ids[:, inputs["input_ids"].shape[1]:] 58 | 59 | # Batch decoding 60 | outputs_list = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) 61 | 62 | for key in inputs: 63 | inputs[key].to('cpu') 64 | output_ids.to('cpu') 65 | del inputs, output_ids 66 | gc.collect() 67 | torch.cuda.empty_cache() 68 | 69 | return outputs_list 70 | 71 | def extend_eos_tokens(self): 72 | # Add closing braces for Vicuna/Llama eos when using attacker model 73 | self.eos_token_ids.extend([ 74 | self.tokenizer.encode("}")[1], 75 | 29913, 76 | 9092, 77 | 16675]) 78 | 79 | class GPT(LanguageModel): 80 | API_RETRY_SLEEP = 10 81 | API_ERROR_OUTPUT = "$ERROR$" 82 | API_QUERY_SLEEP = 2 83 | API_MAX_RETRY = 5 84 | API_TIMEOUT = 20 85 | openai.api_key = os.getenv("OPENAI_API_KEY") 86 | 87 | def generate(self, conv: List[Dict], 88 | max_n_tokens: int, 89 | temperature: float, 90 | top_p: float): 91 | ''' 92 | Args: 93 | conv: List of dictionaries, OpenAI API format 94 | max_n_tokens: int, max number of tokens to generate 95 | temperature: float, temperature for sampling 96 | top_p: float, top p for sampling 97 | Returns: 98 | str: generated response 99 | ''' 100 | output = self.API_ERROR_OUTPUT 101 | for _ in range(self.API_MAX_RETRY): 102 | try: 103 | response = openai.ChatCompletion.create( 104 | model = self.model_name, 105 | messages = conv, 106 | max_tokens = max_n_tokens, 107 | temperature = temperature, 108 | ) 109 | output = response["choices"][0]["message"]["content"] 110 | break 111 | except openai.error.OpenAIError as e: 112 | print(type(e), e) 113 | time.sleep(self.API_RETRY_SLEEP) 114 | 115 | time.sleep(self.API_QUERY_SLEEP) 116 | return output 117 | 118 | def batched_generate(self, 119 | convs_list: List[List[Dict]], 120 | max_n_tokens: int, 121 | temperature: float, 122 | top_p: float = 1.0,): 123 | return [self.generate(conv, max_n_tokens, temperature, top_p) for conv in convs_list] 124 | -------------------------------------------------------------------------------- /dataloaders/bird200.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | from torchvision.datasets.folder import default_loader 4 | from torch.utils.data import Dataset 5 | import numpy as np 6 | import pickle 7 | 8 | 9 | class Cub2011(Dataset): 10 | base_folder = 'CUB_200_2011/images' 11 | 12 | def __init__(self, root, train=True, transform=None, loader=default_loader): 13 | self.root = os.path.expanduser(root) 14 | self.transform = transform 15 | self.loader = default_loader 16 | self.train = train 17 | 18 | self._load_metadata() 19 | 20 | def _load_metadata(self): 21 | images = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'images.txt'), sep=' ', 22 | names=['img_id', 'filepath']) 23 | image_class_labels = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'image_class_labels.txt'), 24 | sep=' ', names=['img_id', 'target']) 25 | train_test_split = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'train_test_split.txt'), 26 | sep=' ', names=['img_id', 'is_training_img']) 27 | 28 | data = images.merge(image_class_labels, on='img_id') 29 | self.data = data.merge(train_test_split, on='img_id') 30 | 31 | if self.train: 32 | self.data = self.data[self.data.is_training_img == 1] 33 | else: 34 | self.data = self.data[self.data.is_training_img == 0] 35 | 36 | class_names = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'classes.txt'), 37 | sep=' ', names=['class_id', 'target']) 38 | self.class_names_str = [name.split(".")[1].replace('_', ' ') for name in class_names.target] 39 | 40 | def __len__(self): 41 | return len(self.data) 42 | 43 | def __getitem__(self, idx): 44 | sample = self.data.iloc[idx] 45 | path = os.path.join(self.root, self.base_folder, sample.filepath) 46 | target = sample.target - 1 # Targets start at 1 by default, so shift to 0 47 | img = self.loader(path) 48 | 49 | if self.transform is not None: 50 | img = self.transform(img) 51 | 52 | return img, target 53 | 54 | 55 | 56 | class Cub100(Cub2011): 57 | def __init__(self, root, train=True, id=True, transform=None, loader=default_loader): 58 | self.id = id 59 | self.root = root 60 | self._select_or_load_classes() 61 | super().__init__(root, train, transform, loader) 62 | 63 | def _select_or_load_classes(self): 64 | subset_classes_file = os.path.join('data', 'CUB-100', "selected_100_classes.pkl") 65 | if os.path.exists(subset_classes_file): 66 | with open(subset_classes_file, 'rb') as f: 67 | self.selected_classes = pickle.load(f) 68 | else: 69 | all_classes = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'classes.txt'), 70 | sep=' ', names=['class_id', 'target']) 71 | selected_class_ids = np.random.choice(all_classes['class_id'], 100, replace=False) 72 | self.selected_classes = all_classes[all_classes['class_id'].isin(selected_class_ids)]['target'].tolist() 73 | # self.selected_classes = all_classes['target'].iloc[:100].tolist() 74 | with open(subset_classes_file, 'wb') as f: 75 | pickle.dump(self.selected_classes, f) 76 | 77 | def _load_metadata(self): 78 | super()._load_metadata() 79 | 80 | all_classes = pd.read_csv(os.path.join(self.root, 'CUB_200_2011', 'classes.txt'), 81 | sep=' ', names=['class_id', 'target']) 82 | 83 | selected_class_ids = all_classes[all_classes['target'].isin(self.selected_classes)]['class_id'].tolist() 84 | 85 | if self.id: # select cub100_iid 86 | self.data = self.data[self.data['target'].isin(selected_class_ids)] 87 | self.class_names_str = [name.split(".")[1].replace('_', ' ') for name in self.selected_classes] 88 | 89 | remaining_class_ids = set(all_classes['class_id']) - set(selected_class_ids) 90 | remaining_class_names = all_classes[all_classes['class_id'].isin(remaining_class_ids)]['target'].tolist() 91 | self.ood_class_name_str = [name.split(".")[1].replace('_', ' ') for name in remaining_class_names] 92 | else: # select cub100_ood 93 | remaining_class_ids = set(all_classes['class_id']) - set(selected_class_ids) 94 | self.data = self.data[self.data['target'].isin(remaining_class_ids)] 95 | remaining_class_names = all_classes[all_classes['class_id'].isin(remaining_class_ids)]['target'].tolist() 96 | self.class_names_str = [name.split(".")[1].replace('_', ' ') for name in remaining_class_names] -------------------------------------------------------------------------------- /envisioned_classes/far_300/car196_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Motorcycle", "Bicycle", "Airplane", "Boat", "Train", "Bus", "Car", "Truck", "Van", "SUV", "Convertible", "Sedan", "Coupe", "Hatchback", "Minivan", "Pickup truck", "Limousine", "Ambulance", "Fire truck", "Police car", "Taxi", "Bulldozer", "Excavator", "Crane", "Forklift", "Tractor", "Trailer truck", "Garbage truck", "Cement mixer truck", "Ice cream truck", "Food truck", "Mail truck", "Tow truck", "School bus", "Double-decker bus", "Tour bus", "RV", "Motorhome", "Jet ski", "Yacht", "Sailboat", "Speedboat", "Canoe", "Kayak", "Submarine", "Helicopter", "Hot air balloon", "Spaceship", "Tram", "Trolleybus- Motorcycle helmet", "Bicycle wheel", "Airplane wing", "Boat anchor", "Train tracks", "Bus stop", "Car keys", "Truck tire", "Van window", "SUV roof", "Convertible top", "Sedan mirror", "Coupe door", "Hatchback trunk", "Minivan sliding door", "Pickup truck bed", "Limousine interior", "Ambulance stretcher", "Fire truck ladder", "Police car siren", "Taxi meter", "Bulldozer blade", "Excavator bucket", "Crane hook", "Forklift pallet", "Tractor wheel", "Trailer truck hitch", "Garbage truck compactor", "Cement mixer drum", "Ice cream truck music", "Food truck menu", "Mail truck mailbox", "Tow truck winch", "School bus stop sign", "Double-decker bus stairs", "Tour bus microphone", "RV awning", "Motorhome kitchen", "Jet ski handlebars", "Yacht anchor", "Sailboat mast", "Speedboat propeller", "Canoe paddle", "Kayak spray skirt", "Submarine periscope", "Helicopter rotor", "Hot air balloon basket", "Spaceship thrusters", "Tram tracks", "Trolleybus overhead wire- Skateboard wheels", "Rollerblade boots", "Surfboard fins", "Snowboard bindings", "Ski poles", "Tennis racket", "Golf club", "Baseball bat", "Basketball hoop", "Soccer ball", "Football helmet", "Volleyball net", "Hockey puck", "Lacrosse stick", "Gymnastics mat", "Yoga mat", "Boxing gloves", "Weightlifting barbell", "Swimming goggles", "Diving mask", "Track and field hurdles", "Cycling helmet", "Running shoes", "Hiking backpack", "Camping tent", "Fishing rod", "Hunting rifle", "Archery bow", "Shooting target", "Skatepark ramp", "Roller coaster", "Ferris wheel", "Carousel horse", "Water slide", "Amusement park ticket", "Concert stage", "Music festival crowd", "Theater curtains", "Dance floor", "Art gallery painting", "Museum sculpture", "Library bookshelf", "Movie theater popcorn", "Arcade game joystick", "Casino slot machine", "Bowling alley pins", "Billiards table", "Karaoke microphone", "Comedy club stage", "Circus tightrope- Coffee cup", "Wine glass", "Plate of food", "Flower bouquet", "Candle holder", "Pillow cushion", "Blanket throw", "Lamp shade", "Mirror frame", "Clock face", "Vase of flowers", "Picture frame", "Plant pot", "Fruit bowl", "Knife and fork", "Spoon and fork", "Wine bottle", "Champagne flute", "Cocktail glass", "Beer mug", "Whiskey glass", "Martini shaker", "Salad bowl", "Soup bowl", "Dessert plate", "Cutting board", "Mixing bowl", "Baking tray", "Oven mitt", "Kitchen scale", "Dish rack", "Dish soap", "Laundry basket", "Ironing board", "Vacuum cleaner", "Mop and bucket", "Broom and dustpan", "Trash can", "Laundry detergent", "Soap dispenser", "Toothbrush holder", "Shower curtain", "Bath towel", "Toilet paper", "Soap dish", "Tissue box", "Hairbrush and comb", "Makeup mirror", "Perfume bottle", "Jewelry box- Laptop computer", "Smartphone screen", "Tablet device", "Television remote", "Camera lens", "Headphones wire", "Speaker system", "Gaming console", "Keyboard and mouse", "Printer paper", "Office desk", "Chair cushion", "File cabinet", "Pen and notebook", "Calculator device", "Whiteboard marker", "Stapler and staples", "Scissors and tape", "Paperclip holder", "Binder and dividers", "Sticky notes pad", "Highlighter pen", "Eraser and pencil", "Backpack straps", "Briefcase handle", "Wallet and keys", "Sunglasses case", "Umbrella handle", "Passport and ticket", "Suitcase wheels", "Shopping cart", "Cash register", "Barcode scanner", "Shopping bag", "Price tag", "Clothing hanger", "Shoe rack", "Jewelry display", "Watch strap", "Sunglasses frame", "Belt buckle", "Wallet and coins", "Perfume tester", "Makeup brushes", "Nail polish bottle", "Hairdryer and brush", "Shampoo bottle", "Soap dispenser", "Toothpaste tube", "Dental floss box- Paintbrush and palette", "Sculpting clay", "Easel and canvas", "Pottery wheel", "Sewing machine", "Knitting needles", "Quilting fabric", "Embroidery hoop", "Beading supplies", "Woodworking tools", "Power drill", "Hammer and nails", "Screwdriver set", "Measuring tape", "Toolbox and wrench", "Safety goggles", "Welding mask", "Soldering iron", "Circuit board", "Microscope slide", "Test tube rack", "Bunsen burner", "Petri dish", "Lab coat", "Safety gloves", "Lab goggles", "Microscope lens", "Pipette dropper", "Lab flask", "Graduated cylinder", "Lab thermometer", "Lab timer", "Lab balance", "Lab centrifuge", "Lab microscope", "Lab petri dish", "Lab test tube", "Lab beaker", "Lab pipette", "Lab microscope slide", "Lab microscope lens", "Lab microscope stage", "Lab microscope objective", "Lab microscope eyepiece", "Lab microscope condenser", "Lab microscope diaphragm", "Lab microscope light source", "Lab microscope focus knob"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/food101_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Orchid", "Watermelon", "Sunflower", "Pineapple", "Kiwi", "Mango", "Avocado", "Papaya", "Dragonfruit", "Guava", "Passionfruit", "Lychee", "Durian", "Jackfruit", "Pomegranate", "Persimmon", "Fig", "Coconut", "Starfruit", "Raspberry", "Blueberry", "Blackberry", "Cranberry", "Strawberry", "Cherry", "Plum", "Apricot", "Peach", "Pear", "Apple", "Orange", "Lemon", "Lime", "Grapefruit", "Banana", "Pinecone", "Acorn", "Walnut", "Chestnut", "Almond", "Pistachio", "Cashew", "Hazelnut", "Peanut", "Macadamia", "Brazil nut", "Pecan", "Walnut", "Sesame seeds- Tulip", "Daisy", "Rose", "Lily", "Sunflower", "Iris", "Orchid", "Carnation", "Daffodil", "Hydrangea", "Peony", "Marigold", "Poppy", "Lavender", "Jasmine", "Magnolia", "Cherry blossom", "Lotus", "Hibiscus", "Cactus", "Succulent", "Fern", "Moss", "Bamboo", "Palm tree", "Maple tree", "Oak tree", "Pine tree", "Willow tree", "Birch tree", "Poplar tree", "Elm tree", "Cypress tree", "Redwood tree", "Sequoia tree", "Cedar tree", "Fir tree", "Spruce tree", "Juniper tree", "Yucca tree", "Agave plant", "Aloe vera", "Venus flytrap", "Carnivorous plant", "Mossy rock", "Pebble beach", "Sandy beach", "Rocky beach", "Coral reef- Waterfall", "Canyon", "Desert", "Glacier", "Volcano", "Mountain", "Forest", "Meadow", "Lake", "River", "Ocean", "Sunset", "Sunrise", "Clouds", "Rainforest", "Savannah", "Tundra", "Prairie", "Wetland", "Marsh", "Dune", "Cliff", "Cave", "Island", "Peninsula", "Archipelago", "Fjord", "Geyser", "Hot spring", "Lagoon", "Oasis", "Reef", "Sand dunes", "Water lily", "Lotus flower", "Mossy tree", "Mossy rock", "Mossy log", "Mossy path", "Mossy wall", "Mossy bridge", "Mossy steps", "Mossy gate", "Mossy fence", "Mossy roof", "Mossy statue", "Mossy bench", "Mossy pond", "Mossy waterfall", "Mossy cave- Desert oasis", "Snow-capped peak", "Rolling hills", "Coastal cliffs", "Tropical rainforest", "Misty waterfall", "Sandy dunes", "Rocky outcrop", "Crystal clear lake", "Dense jungle", "Vibrant coral reef", "Serene pond", "Majestic fjord", "Tranquil riverbank", "Blossoming cherry tree", "Towering redwood", "Pristine beach", "Colorful sunset", "Enchanting forest", "Bubbling hot spring", "Frozen tundra", "Wildflower meadow", "Thundering waterfall", "Serene lakefront", "Misty mountains", "Quaint village", "Rustic farmhouse", "Ancient ruins", "Vibrant cityscape", "Bustling marketplace", "Historic castle", "Modern skyscraper", "Picturesque vineyard", "Charming cottage", "Coastal lighthouse", "Peaceful countryside", "Vibrant street art", "Quaint cobblestone street", "Bustling harbor", "Tranquil garden", "Vibrant flower market", "Serene park", "Majestic canyon", "Pristine island", "Quaint fishing village", "Vibrant festival", "Charming cafe", "Picturesque bridge", "Bustling train station", "Tranquil monastery", "Vibrant carnival- Rustic cabin", "Vibrant marketplace", "Serene waterfall", "Majestic palace", "Quaint bookstore", "Bustling street market", "Tranquil seaside", "Vibrant art gallery", "Charming village square", "Picturesque vineyard", "Bustling cityscape", "Tranquil garden", "Vibrant flower market", "Serene park", "Majestic canyon", "Pristine island", "Quaint fishing village", "Vibrant festival", "Charming cafe", "Picturesque bridge", "Bustling train station", "Tranquil monastery", "Vibrant carnival", "Rustic windmill", "Vibrant mural", "Serene pagoda", "Majestic waterfall", "Quaint tea house", "Bustling night market", "Tranquil rice fields", "Vibrant city park", "Charming seaside town", "Picturesque castle", "Bustling shopping district", "Tranquil zen garden", "Vibrant food market", "Serene lakeside", "Majestic mountain range", "Quaint countryside cottage", "Vibrant cultural festival", "Bustling music concert", "Tranquil beachfront", "Vibrant street performers", "Serene botanical garden", "Majestic ancient ruins", "Quaint coastal village", "Vibrant fashion show", "Bustling amusement park", "Tranquil forest trail", "Vibrant city skyline- Vibrant street market", "Serene countryside landscape", "Majestic cathedral interior", "Quaint mountain village", "Bustling city square", "Tranquil seaside sunset", "Vibrant urban graffiti", "Charming coastal promenade", "Picturesque waterfall hike", "Bustling outdoor concert", "Tranquil lakeside retreat", "Vibrant cultural parade", "Serene botanical oasis", "Majestic coastal cliffs", "Quaint countryside farm", "Vibrant city nightlife", "Bustling food truck festival", "Tranquil forest glade", "Vibrant beach volleyball", "Serene lakeside picnic", "Majestic rooftop skyline", "Quaint riverside walkway", "Vibrant city bike ride", "Bustling farmers market", "Tranquil garden tea ceremony", "Vibrant art museum", "Serene mountain peak", "Majestic palace gardens", "Quaint village fair", "Vibrant city marathon", "Bustling shopping mall", "Tranquil yoga retreat", "Vibrant street food", "Serene lakeside cabin", "Majestic coastal lighthouse", "Quaint countryside vineyard", "Vibrant city tram ride", "Bustling music festival", "Tranquil forest meditation", "Vibrant beach volleyball", "Serene lakeside picnic", "Majestic rooftop skyline", "Quaint riverside walkway", "Vibrant city bike ride", "Bustling farmers market", "Tranquil garden tea ceremony", "Vibrant art museum", "Serene mountain peak"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/pet18_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Balinese", "Oriental Shorthair", "American Shorthair", "British Shorthair", "Exotic Shorthair", "Chartreux", "Turkish Angora", "Turkish Van", "Norwegian Forest", "Siberian", "Somali", "Ocicat", "American Bobtail", "Manx", "American Curl", "Selkirk Rex", "LaPerm", "Havana Brown", "Singapura", "Egyptian Mau", "Bengal", "Savannah", "Toyger", "Pixiebob", "Ragamuffin", "Munchkin", "Sphynx", "Peterbald", "Lykoi", "Australian Mist", "Chausie", "Cheetoh", "Donskoy", "Highlander", "Kurilian Bobtail", "Serengeti", "Thai", "Ukrainian Levkoy", "Caracat", "Caracal- Afghan Hound", "Airedale Terrier", "American Eskimo Dog", "Australian Cattle Dog", "Basenji", "Bichon Frise", "Bloodhound", "Border Terrier", "Boston Terrier", "Boxer", "Brittany Spaniel", "Bull Terrier", "Cairn Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chihuahua", "Chinese Crested", "Chow Chow", "Clumber Spaniel", "Cocker Spaniel", "Collie", "Dalmatian", "Dandie Dinmont Terrier", "English Bulldog", "English Setter", "English Springer Spaniel", "French Bulldog", "German Shorthaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Gordon Setter", "Great Dane", "Great Pyrenees", "Greyhound", "Irish Setter", "Irish Wolfhound", "Italian Greyhound", "Jack Russell Terrier", "Japanese Chin", "Keeshond", "Kerry Blue Terrier", "King Charles Spaniel", "Komondor", "Kuvasz", "Lhasa Apso", "Maltese", "Manchester Terrier", "Mastiff", "Miniature Bull Terrier- Newfoundland", "Old English Sheepdog", "Papillon", "Pekingese", "Pembroke Welsh Corgi", "Petit Basset Griffon Vendeen", "Pharaoh Hound", "Plott Hound", "Pointer", "Polish Lowland Sheepdog", "Pomeranian", "Portuguese Water Dog", "Rhodesian Ridgeback", "Rottweiler", "Saluki", "Samoyed", "Schipperke", "Scottish Deerhound", "Scottish Terrier", "Shar Pei", "Shetland Sheepdog", "Shih Tzu", "Siberian Husky", "Silky Terrier", "Skye Terrier", "Smooth Fox Terrier", "Soft Coated Wheaten Terrier", "Spinone Italiano", "Staffordshire Bull Terrier", "Standard Schnauzer", "Sussex Spaniel", "Tibetan Mastiff", "Tibetan Spaniel", "Tibetan Terrier", "Toy Fox Terrier", "Vizsla", "Weimaraner", "Welsh Springer Spaniel", "Welsh Terrier", "West Highland White Terrier", "Whippet", "Wire Fox Terrier", "Wirehaired Pointing Griffon", "Xoloitzcuintli", "Yorkshire Terrier", "Abyssinian", "American Curl", "American Shorthair", "American Wirehair", "Balinese", "Birman- Bombay", "British Shorthair", "Burmese", "Chartreux", "Cornish Rex", "Devon Rex", "Egyptian Mau", "Exotic Shorthair", "Havana Brown", "Japanese Bobtail", "Korat", "LaPerm", "Maine Coon", "Manx", "Norwegian Forest Cat", "Ocicat", "Oriental Shorthair", "Persian", "Pixiebob", "Ragamuffin", "Ragdoll", "Russian Blue", "Scottish Fold", "Selkirk Rex", "Siamese", "Siberian", "Singapura", "Snowshoe", "Somali", "Sphynx", "Tonkinese", "Turkish Angora", "Turkish Van", "American Eskimo Dog", "Australian Cattle Dog", "Basset Hound", "Bearded Collie", "Belgian Malinois", "Bernese Mountain Dog", "Border Collie", "Borzoi", "Boston Terrier", "Boxer", "Bullmastiff", "Chinese Crested", "Chow Chow", "Dalmatian", "English Setter", "Finnish Spitz", "Flat-Coated Retriever- Affenpinscher", "American Staffordshire Terrier", "Anatolian Shepherd Dog", "Australian Terrier", "Azawakh", "Barbet", "Belgian Sheepdog", "Belgian Tervuren", "Bergamasco", "Black and Tan Coonhound", "Black Russian Terrier", "Bluetick Coonhound", "Boerboel", "Bouvier des Flandres", "Bracco Italiano", "Briard", "Bull Terrier (Miniature)", "Canaan Dog", "Cardigan Welsh Corgi", "Catahoula Leopard Dog", "Caucasian Shepherd Dog", "Chesapeake Bay Retriever", "Chinese Shar-Pei", "Cirneco dell'Etna", "Clumber Spaniel", "Curly-Coated Retriever", "Czechoslovakian Vlcak", "Dandie Dinmont Terrier", "Danish-Swedish Farmdog", "Dingo", "Dutch Shepherd", "English Foxhound", "Entlebucher Mountain Dog", "Estrela Mountain Dog", "Eurasier", "Field Spaniel", "Finnish Lapphund", "Finnish Spitz", "Flat-Coated Retriever", "French Spaniel", "German Pinscher", "German Wirehaired Pointer", "Glen of Imaal Terrier", "Golden Retriever", "Gordon Setter", "Grand Basset Griffon Vendeen", "Great Dane", "Greater Swiss Mountain Dog- Harrier", "Ibizan Hound", "Icelandic Sheepdog", "Irish Red and White Setter", "Irish Terrier", "Irish Water Spaniel", "Irish Wolfhound", "Italian Greyhound", "Japanese Spitz", "Kai Ken", "Karelian Bear Dog", "Klee Kai", "Kooikerhondje", "Kromfohrlander", "Kuvasz", "Lagotto Romagnolo", "Lakeland Terrier", "Lancashire Heeler", "Leonberger", "Lhasa Apso", "Lowchen", "Maltese", "Manchester Terrier", "Maremma Sheepdog", "Mexican Hairless Dog", "Miniature Bull Terrier", "Miniature Pinscher", "Miniature Schnauzer", "Neapolitan Mastiff", "Newfoundland", "Norfolk Terrier", "Norwegian Buhund", "Norwegian Elkhound", "Norwich Terrier", "Nova Scotia Duck Tolling Retriever", "Old English Sheepdog", "Otterhound", "Papillon", "Parson Russell Terrier", "Pekingese", "Pembroke Welsh Corgi", "Peruvian Inca Orchid", "Petit Basset Griffon Vendeen", "Pharaoh Hound", "Plott", "Pointer", "Polish Lowland Sheepdog", "Pomeranian"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/pet18_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Balinese", "Oriental Shorthair", "Turkish Angora", "Turkish Van", "Somali", "Chartreux", "American Shorthair", "British Shorthair", "Exotic Shorthair", "Himalayan", "Manx", "Norwegian Forest", "Siberian", "Singapura", "Ocicat", "American Curl", "Selkirk Rex", "LaPerm", "Peterbald", "Havana Brown", "American Wirehair", "Egyptian Mau", "Toyger", "Munchkin", "Pixiebob", "Ragamuffin", "Ragdoll", "Scottish Fold", "Scottish Straight", "Selkirk Rex", "Serengeti", "Serval", "Snowshoe", "Sokoke", "Somali", "Sphynx", "Thai", "Tiffany", "Tonkinese", "Toyger- Australian Cattle Dog", "Bernese Mountain Dog", "Bloodhound", "Border Terrier", "Boston Terrier", "Boxer", "Bull Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chihuahua", "Chinese Crested", "Chow Chow", "Cocker Spaniel", "Collie", "Dalmatian", "English Bulldog", "French Bulldog", "German Shorthaired Pointer", "Great Dane", "Greyhound", "Irish Setter", "Italian Greyhound", "Jack Russell Terrier", "King Charles Spaniel", "Lhasa Apso", "Maltese", "Miniature Pinscher", "Newfoundland", "Papillon", "Pomeranian", "Pug", "Rhodesian Ridgeback", "Rottweiler", "Saint Bernard", "Shar Pei", "Shetland Sheepdog", "Shih Tzu", "Siberian Husky", "Staffordshire Bull Terrier", "Tibetan Mastiff", "Vizsla", "Weimaraner", "Welsh Corgi", "West Highland White Terrier", "Whippet", "Yorkshire Terrier", "Abyssinian Cat", "American Bobtail", "American Curl Cat", "American Shorthair Cat", "American Wirehair Cat- Afghan Hound", "Airedale Terrier", "Akita", "Alaskan Malamute", "American Eskimo Dog", "Australian Shepherd", "Basenji", "Beagle", "Belgian Malinois", "Belgian Sheepdog", "Belgian Tervuren", "Bichon Frise", "Border Collie", "Borzoi", "Bouvier des Flandres", "Brittany Spaniel", "Brussels Griffon", "Bullmastiff", "Cairn Terrier", "Cardigan Welsh Corgi", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chinese Crested", "Chinese Shar-Pei", "Chow Chow", "Clumber Spaniel", "Cocker Spaniel", "Collie", "Dalmatian", "Dandie Dinmont Terrier", "Doberman Pinscher", "English Cocker Spaniel", "English Foxhound", "English Setter", "English Springer Spaniel", "Field Spaniel", "Finnish Spitz", "Flat-Coated Retriever", "French Bulldog", "German Pinscher", "German Shepherd Dog", "German Shorthaired Pointer", "German Wirehaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Golden Retriever", "Gordon Setter", "Great Dane- Havana Brown", "Himalayan", "Irish Wolfhound", "Japanese Bobtail", "Japanese Chin", "Japanese Spitz", "Keeshond", "Kerry Blue Terrier", "Komondor", "Kuvasz", "Leonberger", "Lowchen", "Maltese", "Manchester Terrier", "Miniature Bull Terrier", "Miniature Pinscher", "Neapolitan Mastiff", "Norwegian Buhund", "Norwegian Elkhound", "Norwegian Lundehund", "Norwich Terrier", "Nova Scotia Duck Tolling Retriever", "Old English Sheepdog", "Otterhound", "Papillon", "Parson Russell Terrier", "Pembroke Welsh Corgi", "Petit Basset Griffon Vendeen", "Pharaoh Hound", "Plott", "Pointer", "Polish Lowland Sheepdog", "Pomeranian", "Portuguese Water Dog", "Puli", "Pumi", "Redbone Coonhound", "Rhodesian Ridgeback", "Rottweiler", "Saluki", "Samoyed", "Schipperke", "Scottish Deerhound", "Scottish Terrier", "Sealyham Terrier", "Shetland Sheepdog", "Shiba Inu", "Shih Tzu", "Siberian Husky- Singapura", "Skye Terrier", "Sloughi", "Smooth Fox Terrier", "Soft-Coated Wheaten Terrier", "Spanish Water Dog", "Spinone Italiano", "Staffordshire Bull Terrier", "Standard Schnauzer", "Sussex Spaniel", "Swedish Vallhund", "Tibetan Mastiff", "Tibetan Spaniel", "Tibetan Terrier", "Toy Fox Terrier", "Treeing Walker Coonhound", "Vizsla", "Weimaraner", "Welsh Springer Spaniel", "Welsh Terrier", "West Highland White Terrier", "Whippet", "Wire Fox Terrier", "Wirehaired Pointing Griffon", "Xoloitzcuintli", "Yorkshire Terrier", "American Curl", "American Shorthair", "American Wirehair", "Balinese", "Birman", "Bombay", "British Shorthair", "Burmese", "Chartreux", "Cornish Rex", "Devon Rex", "Egyptian Mau", "European Burmese", "Exotic Shorthair", "Havana Brown", "Japanese Bobtail", "Korat", "LaPerm", "Maine Coon", "Manx", "Norwegian Forest Cat", "Ocicat- American Eskimo Dog", "Anatolian Shepherd Dog", "Australian Cattle Dog", "Australian Terrier", "Bearded Collie", "Belgian Laekenois", "Belgian Malinois", "Belgian Sheepdog", "Belgian Tervuren", "Bernese Mountain Dog", "Bichon Frise", "Black Russian Terrier", "Border Terrier", "Bouvier des Flandres", "Briard", "Brussels Griffon", "Cairn Terrier", "Cardigan Welsh Corgi", "Chesapeake Bay Retriever", "Chinese Crested", "Chinese Shar-Pei", "Clumber Spaniel", "Cocker Spaniel", "Curly-Coated Retriever", "Dandie Dinmont Terrier", "English Setter", "English Toy Spaniel", "Field Spaniel", "Finnish Lapphund", "Finnish Spitz", "Flat-Coated Retriever", "Glen of Imaal Terrier", "Gordon Setter", "Great Pyrenees", "Greater Swiss Mountain Dog", "Havanese", "Ibizan Hound", "Irish Red and White Setter", "Irish Terrier", "Irish Water Spaniel", "Irish Wolfhound", "Italian Greyhound", "Japanese Chin", "Keeshond", "Kerry Blue Terrier", "Komondor", "Kuvasz", "Labrador Retriever"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/pet18_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["Siamese", "Persian", "Abyssinian", "Scottish Fold", "Russian Blue", "Devon Rex", "Cornish Rex", "Burmese", "Birman", "Tonkinese", "Balinese", "Oriental Shorthair", "Turkish Angora", "Turkish Van", "Somali", "Chartreux", "American Shorthair", "British Shorthair", "Exotic Shorthair", "Himalayan", "Manx", "Norwegian Forest", "Siberian", "Singapura", "Ocicat", "American Curl", "Selkirk Rex", "LaPerm", "Peterbald", "Havana Brown", "Egyptian Mau", "Bengal", "Savannah", "Toyger", "Serengeti", "Caracal", "Lynx", "Ocelot", "Serval", "Cheetah", "Leopard", "Lion", "Tiger", "Jaguar", "Panther", "Snow Leopard", "Clouded Leopard", "Puma", "Bobcat", "Cougar", "Lynx- Australian Cattle Dog", "Bernese Mountain Dog", "Bloodhound", "Border Terrier", "Boston Terrier", "Boxer", "Bull Terrier", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chihuahua", "Chinese Crested", "Chow Chow", "Cocker Spaniel", "Collie", "Dalmatian", "English Bulldog", "French Bulldog", "German Shorthaired Pointer", "Great Dane", "Greyhound", "Irish Setter", "Italian Greyhound", "Jack Russell Terrier", "Maltese", "Miniature Pinscher", "Newfoundland", "Papillon", "Pomeranian", "Pug", "Rhodesian Ridgeback", "Rottweiler", "Saint Bernard", "Shar Pei", "Shih Tzu", "Siberian Husky", "Staffordshire Bull Terrier", "Vizsla", "Weimaraner", "Welsh Corgi", "West Highland White Terrier", "Whippet", "Yorkshire Terrier", "Abyssinian Cat", "American Bobtail", "American Curl Cat", "American Shorthair Cat", "Balinese Cat", "Birman Cat", "Bombay Cat", "British Shorthair Cat", "Burmese Cat- Afghan Hound", "Airedale Terrier", "Akita", "Alaskan Malamute", "American Eskimo Dog", "Australian Shepherd", "Basenji", "Beagle", "Belgian Malinois", "Belgian Sheepdog", "Belgian Tervuren", "Bichon Frise", "Border Collie", "Borzoi", "Bouvier des Flandres", "Brittany Spaniel", "Brussels Griffon", "Bullmastiff", "Cairn Terrier", "Cardigan Welsh Corgi", "Cavalier King Charles Spaniel", "Chesapeake Bay Retriever", "Chinese Crested", "Chinese Shar-Pei", "Chow Chow", "Clumber Spaniel", "Cocker Spaniel", "Collie", "Dalmatian", "Dandie Dinmont Terrier", "Doberman Pinscher", "English Cocker Spaniel", "English Foxhound", "English Setter", "English Springer Spaniel", "Field Spaniel", "Finnish Spitz", "Flat-Coated Retriever", "French Bulldog", "German Pinscher", "German Shorthaired Pointer", "German Wirehaired Pointer", "Giant Schnauzer", "Glen of Imaal Terrier", "Golden Retriever", "Gordon Setter", "Great Dane", "Great Pyrenees- Havana Brown Cat", "Himalayan Cat", "Irish Wolfhound", "Japanese Bobtail Cat", "Japanese Chin", "Keeshond", "Kerry Blue Terrier", "Komondor", "Kuvasz", "Labrador Retriever", "Leonberger", "Lhasa Apso", "Lowchen", "Maltese Dog", "Manchester Terrier", "Mastiff", "Miniature Bull Terrier", "Miniature Schnauzer", "Neapolitan Mastiff", "Norfolk Terrier", "Norwegian Buhund", "Norwegian Elkhound", "Norwich Terrier", "Nova Scotia Duck Tolling Retriever", "Old English Sheepdog", "Otterhound", "Papillon Dog", "Parson Russell Terrier", "Pekingese", "Pembroke Welsh Corgi", "Petit Basset Griffon Vendeen", "Pharaoh Hound", "Plott Hound", "Pointer", "Polish Lowland Sheepdog", "Pomeranian Dog", "Portuguese Water Dog", "Puli", "Redbone Coonhound", "Rhodesian Ridgeback Dog", "Rottweiler Dog", "Saluki", "Samoyed Dog", "Schipperke", "Scottish Deerhound", "Scottish Terrier", "Sealyham Terrier", "Shetland Sheepdog", "Shiba Inu- Shih Tzu Dog", "Siberian Husky Dog", "Silky Terrier", "Skye Terrier", "Sloughi", "Small Munsterlander Pointer", "Smooth Fox Terrier", "Soft Coated Wheaten Terrier", "Spanish Water Dog", "Spinone Italiano", "Staffordshire Bull Terrier", "Standard Schnauzer", "Sussex Spaniel", "Swedish Vallhund", "Tibetan Mastiff", "Tibetan Spaniel", "Tibetan Terrier", "Toy Fox Terrier", "Treeing Walker Coonhound", "Vizsla Dog", "Weimaraner Dog", "Welsh Springer Spaniel", "Welsh Terrier", "West Highland White Terrier", "Whippet Dog", "Wire Fox Terrier", "Wirehaired Pointing Griffon", "Xoloitzcuintli", "Yorkshire Terrier Dog", "American Curl Cat", "American Shorthair Cat", "Balinese Cat", "Birman Cat", "Bombay Cat", "British Shorthair Cat", "Burmese Cat", "Chartreux Cat", "Cornish Rex Cat", "Devon Rex Cat", "Egyptian Mau Cat", "Exotic Shorthair Cat", "Havana Brown Cat", "Japanese Bobtail Cat", "Korat Cat", "Maine Coon Cat", "Manx Cat", "Norwegian Forest Cat", "Ocicat", "Oriental Shorthair Cat", "Persian Cat- Abyssinian Cat", "American Bobtail Cat", "American Curl Cat", "American Wirehair Cat", "Australian Mist Cat", "Balinese Cat", "Bengal Cat", "Birman Cat", "Bombay Cat", "British Longhair Cat", "British Shorthair Cat", "Burmese Cat", "Burmilla Cat", "Chartreux Cat", "Chausie Cat", "Cornish Rex Cat", "Cymric Cat", "Devon Rex Cat", "Egyptian Mau Cat", "European Shorthair Cat", "Exotic Shorthair Cat", "Havana Brown Cat", "Highlander Cat", "Japanese Bobtail Cat", "Javanese Cat", "Korat Cat", "LaPerm Cat", "Maine Coon Cat", "Manx Cat", "Munchkin Cat", "Nebelung Cat", "Norwegian Forest Cat", "Ocicat", "Oriental Longhair Cat", "Oriental Shorthair Cat", "Persian Cat", "Peterbald Cat", "Pixiebob Cat", "Ragamuffin Cat", "Ragdoll Cat", "Russian Blue Cat", "Savannah Cat", "Scottish Fold Cat", "Selkirk Rex Cat", "Siamese Cat", "Siberian Cat", "Singapura Cat", "Snowshoe Cat", "Somali Cat"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/pet37_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Dolphin", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Fireworks", "Butterfly", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China", "Machu Picchu", "Sydney Opera House", "Mount Everest", "Niagara Falls", "Grand Canyon", "Venice canals", "Santorini", "African safari", "Amazon rainforest", "Taj Mahal", "Stonehenge", "Easter Island", "Pyramids of Giza", "Colosseum", "Mount Fuji", "Petra", "Angkor Wat", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China- Waterfall", "Sunset", "Cherry Blossom", "Aurora Borealis", "Desert Oasis", "Tropical Beach", "Snowy Mountain", "Rolling Hills", "City Skyline", "Countryside Farm", "Ocean Waves", "Starry Night", "Autumn Forest", "Spring Meadow", "Rocky Coastline", "Wildflower Field", "Thunderstorm", "Iceberg", "Coral Reef", "Sand Dunes", "Bamboo Forest", "Vineyard Landscape", "Hot Air Balloon", "Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Car", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Art", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway- Cherry Blossoms", "Autumn Leaves", "Snowy Peaks", "Rolling Hills", "Coastal Sunset", "Tropical Paradise", "Desert Sands", "City Skylines", "Countryside Farmhouse", "Ocean Waves", "Starry Night", "Spring Blossoms", "Rocky Cliffs", "Wildflower Meadow", "Thunderstorm Clouds", "Iceberg Landscape", "Coral Reef Life", "Sand Dune Adventure", "Bamboo Forest", "Vineyard Rows", "Hot Air Balloon", "Colorful Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Cars", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Artwork", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway", "Mountain Peaks", "Coastal Cliffs", "Vibrant Sunsets", "Tranquil Lakes- Cherry Blossom", "Autumn Leaves", "Snowy Peaks", "Rolling Hills", "Coastal Sunset", "Tropical Paradise", "Desert Sands", "City Skylines", "Countryside Farmhouse", "Ocean Waves", "Starry Night", "Spring Blossoms", "Rocky Cliffs", "Wildflower Meadow", "Thunderstorm Clouds", "Iceberg Landscape", "Coral Reef Life", "Sand Dune Adventure", "Bamboo Forest", "Vineyard Rows", "Hot Air Balloon", "Colorful Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Cars", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Artwork", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway", "Mountain Peaks", "Coastal Cliffs", "Vibrant Sunsets", "Tranquil Lakes- Cherry Blossom", "Autumn Leaves", "Snowy Peaks", "Rolling Hills", "Coastal Sunset", "Tropical Paradise", "Desert Sands", "City Skylines", "Countryside Farmhouse", "Ocean Waves", "Starry Night", "Spring Blossoms", "Rocky Cliffs", "Wildflower Meadow", "Thunderstorm Clouds", "Iceberg Landscape", "Coral Reef Life", "Sand Dune Adventure", "Bamboo Forest", "Vineyard Rows", "Hot Air Balloon", "Colorful Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Cars", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Artwork", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway", "Mountain Peaks", "Coastal Cliffs", "Vibrant Sunsets", "Tranquil Lakes- Cherry Blossom", "Autumn Leaves", "Snowy Peaks", "Rolling Hills", "Coastal Sunset", "Tropical Paradise", "Desert Sands", "City Skylines", "Countryside Farmhouse", "Ocean Waves", "Starry Night", "Spring Blossoms", "Rocky Cliffs", "Wildflower Meadow", "Thunderstorm Clouds", "Iceberg Landscape", "Coral Reef Life", "Sand Dune Adventure", "Bamboo Forest", "Vineyard Rows", "Hot Air Balloon", "Colorful Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Cars", "Wildlife Safari", "Exotic Birds", "Underwater World", "Deep Space", "Abstract Artwork", "Geometric Patterns", "Macro Photography", "Reflections on Water", "Silhouette Sunset", "Misty Forest", "Rustic Cabin", "Zen Garden", "Floating Market", "Street Food", "Artisan Crafts", "Fashion Runway", "Mountain Peaks", "Coastal Cliffs", "Vibrant Sunsets", "Tranquil Lakes"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/pet37_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Dolphin", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Fireworks", "Butterfly", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China", "Machu Picchu", "Sydney Opera House", "Mount Everest", "Niagara Falls", "Grand Canyon", "Venice canals", "Santorini", "African safari", "Amazon rainforest", "Taj Mahal", "Stonehenge", "Easter Island", "Pyramids of Giza", "Colosseum", "Mount Fuji", "Petra", "Angkor Wat", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China- Waterfall", "Sunset", "Cherry Blossom", "Aurora Borealis", "Desert Oasis", "Tropical Beach", "Snowy Mountain", "Rolling Hills", "City Skyline", "Countryside Farm", "Ocean Waves", "Starry Night", "Autumn Forest", "Spring Meadow", "Rocky Coastline", "Wildflower Field", "Thunderstorm", "Iceberg", "Coral Reef", "Sand Dunes", "Bamboo Forest", "Vineyard Landscape", "Hot Air Balloon", "Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Car", "Wildlife Safari", "Exotic Birds", "Marine Life", "Farm Animals", "Desert Wildlife", "Rainforest Canopy", "Snowy Owl", "Polar Bear", "Cheetah Running", "Elephant Herd", "Whale Breaching", "Dolphin Pod", "Sea Turtle", "Jellyfish Swarm", "Colorful Fish", "Orchid Garden- Cherry Blossoms", "Golden Sunset", "Snow-Capped Peaks", "Rolling Meadows", "Urban Skylines", "Coastal Cliffs", "Tropical Paradise", "Desert Landscapes", "Crystal Clear Waters", "Vibrant Sunsets", "Enchanting Forests", "Majestic Waterfalls", "Serene Lakes", "Tranquil Rivers", "Pristine Beaches", "Dramatic Canyons", "Ancient Temples", "Medieval Castles", "Futuristic Cities", "Graffiti Artwork", "Vintage Streets", "Classic Cars", "Wildlife Safaris", "Exotic Birds", "Marine Creatures", "Endangered Species", "Rainforest Canopies", "Arctic Landscapes", "African Savannas", "Coral Reefs", "Underwater Wonders", "Mountain Ranges", "Colorful Orchids", "Blossoming Gardens", "Zen Gardens", "Majestic Palaces", "Iconic Landmarks", "Architectural Marvels", "Street Markets", "Festive Celebrations", "Cultural Festivals", "Artistic Sculptures", "Abstract Paintings", "Street Performers", "Ballet Performances", "Symphony Orchestras", "Jazz Bands", "Flamenco Dancers", "Circus Acrobats", "Broadway Musicals- Misty Forest", "Rustic Barns", "Coastal Sunsets", "Snowy Landscapes", "Urban Skylines", "Tropical Islands", "Desert Dunes", "Crystal Clear Lakes", "Vibrant Gardens", "Enchanting Waterfalls", "Serene Rivers", "Pristine Beaches", "Majestic Mountains", "Tranquil Countryside", "Dramatic Cliffs", "Ancient Ruins", "Medieval Castles", "Futuristic Architecture", "Street Art", "Vintage Cars", "Wildlife Reserves", "Exotic Birds", "Marine Life", "Endangered Species", "Rainforest Canopies", "Arctic Landscapes", "African Safaris", "Coral Reefs", "Underwater Life", "Mountain Peaks", "Colorful Flowers", "Blossoming Orchards", "Zen Gardens", "Majestic Palaces", "Iconic Landmarks", "Architectural Wonders", "Street Markets", "Festive Celebrations", "Cultural Festivals", "Artistic Sculptures", "Abstract Paintings", "Street Performers", "Ballet Performances", "Symphony Orchestras", "Jazz Bands", "Flamenco Dancers", "Circus Acts", "Broadway Shows- Misty Waterfalls", "Rustic Cabins", "Coastal Sunrises", "Snowy Peaks", "Urban Skylines", "Tropical Rainforests", "Desert Landscapes", "Crystal Clear Rivers", "Vibrant Wildflowers", "Enchanting Sunsets", "Serene Lakeside", "Pristine Coastlines", "Majestic Valleys", "Tranquil Meadows", "Dramatic Sunrises", "Ancient Temples", "Medieval Cathedrals", "Futuristic Skyscrapers", "Street Murals", "Vintage Motorcycles", "Wildlife Sanctuaries", "Exotic Butterflies", "Marine Ecosystems", "Endangered Wildlife", "Rainforest Canopies", "Arctic Tundra", "African Plains", "Coral Gardens", "Underwater Caves", "Mountain Trails", "Colorful Balloons", "Blossoming Cherry Trees", "Zen Meditation", "Majestic Water Palaces", "Iconic Bridges", "Architectural Marvels", "Street Food", "Festive Parades", "Cultural Dances", "Artistic Installations", "Abstract Sculptures", "Street Musicians", "Ballet Performances", "Symphony Concerts", "Jazz Festivals", "Flamenco Performances", "Circus Troupes", "Broadway Productions- Misty Waterfalls", "Rustic Cabins", "Coastal Sunrises", "Snowy Peaks", "Urban Skylines", "Tropical Rainforests", "Desert Landscapes", "Crystal Clear Rivers", "Vibrant Wildflowers", "Enchanting Sunsets", "Serene Lakeside", "Pristine Coastlines", "Majestic Valleys", "Tranquil Meadows", "Dramatic Sunrises", "Ancient Temples", "Medieval Cathedrals", "Futuristic Skyscrapers", "Street Murals", "Vintage Motorcycles", "Wildlife Sanctuaries", "Exotic Butterflies", "Marine Ecosystems", "Endangered Wildlife", "Rainforest Canopies", "Arctic Tundra", "African Plains", "Coral Gardens", "Underwater Caves", "Mountain Trails", "Colorful Balloons", "Blossoming Cherry Trees", "Zen Meditation", "Majestic Water Palaces", "Iconic Bridges", "Architectural Marvels", "Street Food", "Festive Parades", "Cultural Dances", "Artistic Installations", "Abstract Sculptures", "Street Musicians", "Ballet Performances", "Symphony Concerts", "Jazz Festivals", "Flamenco Performances", "Circus Troupes", "Broadway Productions"]} -------------------------------------------------------------------------------- /envisioned_classes/far_300/pet37_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"far": ["Sunflower", "Waterfall", "Giraffe", "Coral reef", "Snowy mountain", "Palm tree", "Desert landscape", "Peacock", "Aurora borealis", "Jellyfish", "Redwood tree", "Cactus", "Koala", "Cherry blossom", "Elephant", "Rainbow", "Lighthouse", "Dolphin", "Autumn leaves", "Penguin", "Cherry pie", "Hot air balloon", "Fireworks", "Butterfly", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China", "Machu Picchu", "Sydney Opera House", "Mount Everest", "Niagara Falls", "Grand Canyon", "Venice canals", "Santorini", "African safari", "Amazon rainforest", "Taj Mahal", "Stonehenge", "Easter Island", "Pyramids of Giza", "Colosseum", "Mount Fuji", "Petra", "Angkor Wat", "Taj Mahal", "Statue of Liberty", "Eiffel Tower", "Great Wall of China- Waterfall", "Sunset", "Cherry Blossom", "Aurora Borealis", "Desert Oasis", "Tropical Beach", "Snowy Mountain", "Rolling Hills", "City Skyline", "Countryside Farm", "Ocean Waves", "Starry Night", "Autumn Forest", "Spring Meadow", "Rocky Coastline", "Wildflower Field", "Thunderstorm", "Iceberg", "Coral Reef", "Sand Dunes", "Bamboo Forest", "Vineyard Landscape", "Hot Air Balloon", "Rainbow", "Fireworks Display", "Butterfly Garden", "Underwater Cave", "Ancient Ruins", "Castle Fortress", "Modern Architecture", "Street Graffiti", "Neon Lights", "Vintage Car", "Wildlife Safari", "Exotic Birds", "Marine Life", "Desert Cactus", "Alpine Lake", "Zen Garden", "Water Lily Pond", "Rolling Fog", "Countryside Cottage", "Urban Street Art", "Snowy Forest", "Coastal Cliffs", "Lavender Field", "Cherry Orchard", "Bamboo Grove- Sunrise", "Misty Forest", "Tropical Paradise", "Snow-Capped Peaks", "Urban Skylines", "Rustic Barns", "Secluded Beaches", "Vibrant Sunsets", "Rolling Vineyards", "Majestic Waterfalls", "Enchanting Gardens", "Serene Lakes", "Whimsical Street Art", "Ancient Temples", "Coastal Lighthouses", "Tranquil Rivers", "Colorful Hot Air Balloons", "Pristine Coral Reefs", "Towering Sand Dunes", "Bamboo Groves", "Charming Villages", "Urban Rooftops", "Graffiti Art", "Vintage Markets", "Wildlife Reserves", "Desert Landscapes", "Alpine Meadows", "Zen Rock Gardens", "Cherry Blossom Gardens", "Lavender Fields", "Coastal Cliffs", "Mountain Reflections", "Urban 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"Walnut", "Chestnut", "Almond", "Pistachio", "Cashew", "Hazelnut", "Macadamia", "Peanut", "Pecan", "Brazil nut", "Walnut", "Butternut squash- Sunset", "Waterfall", "Beach", "Forest", "Mountain range", "Desert", "Canyon", "Glacier", "Volcano", "Aurora borealis", "Rainbow", "Clouds", "Waves", "Snowflakes", "Lightning", "Stars", "Moon", "Sun", "Sand dunes", "Tornado", "Tsunami", "Thunderstorm", "Fog", "Mist", "Iceberg", "Coral reef", "Water droplets", "Autumn leaves", "Spring blossoms", "Summer meadow", "Winter wonderland", "City skyline", "Countryside", "Farm animals", "Wild animals", "Birds in flight", "Underwater world", "Insects", "Reptiles", "Amphibians", "Butterflies", "Flowers in bloom", "Fruits in a basket", "Vegetables in a garden", "Herbs and spices", "Coffee beans", "Tea leaves", "Wine bottles", "Whiskey glasses", "Beer mugs- Abstract art", "Geometric shapes", "Neon lights", "Graffiti walls", "Vintage cars", "Modern architecture", "Street fashion", "Abstract patterns", "Neon 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landscapes- Abstract paintings", "Geometric structures", "Neon cityscapes", "Graffiti tags", "Vintage aesthetics", "Modern sculptures", "Street murals", "Abstract designs", "Neon lights", "Industrial landscapes", "Urban photography", "Retro fashion", "Minimalist art", "Contemporary installations", "Pop art prints", "Street performers", "Abstract patterns", "Futuristic architecture", "Surreal artwork", "Macro photography", "Architectural details", "Vintage cars", "Abstract portraits", "Urban exploration", "Geometric shapes", "Abstract murals", "Retro technology", "Industrial machinery", "Contemporary dance", "Street musicians", "Abstract collages", "Urban decay", "Geometric patterns", "Abstract sculptures", "Retro advertisements", "Industrial interiors", "Contemporary installations", "Abstract textiles", "Graffiti art", "Modern sculptures", "Street food", "Abstract ceramics", "Neon graffiti", "Vintage vinyl records", "Industrial cityscapes", "Contemporary installations", "Abstract jewelry", "Urban transportation", "Retro typography", "Abstract glass art", "Neon fashion", "Vintage motorcycles", "Industrial landscapes- Abstract drawings", "Geometric patterns", "Neon signs", "Graffiti art", "Vintage fashion", "Modern sculptures", "Street art", "Abstract designs", "Neon lights", "Industrial landscapes", "Urban photography", "Retro aesthetics", "Minimalist art", "Contemporary installations", "Pop art prints", "Street performers", "Abstract patterns", "Futuristic architecture", "Surreal artwork", "Macro photography", "Architectural details", "Vintage cars", "Abstract portraits", "Urban exploration", "Geometric shapes", "Abstract murals", "Retro technology", "Industrial machinery", "Contemporary dance", "Street musicians", "Abstract collages", "Urban decay", "Geometric patterns", "Abstract sculptures", "Retro advertisements", "Industrial interiors", "Contemporary installations", "Abstract textiles", "Graffiti murals", "Modern sculptures", "Street food", "Abstract 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"Abstract drawings", "Street festivals", "Industrial design", "Cityscape", "Urban landscapes", "Vintage records", "Retro hairstyles", "Abstract textiles", "Street fashion"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/cub100_ID_gpt-3.5-turbo-16k_0.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian Goose", "Ivory-billed Woodpecker", "Jamaican Tody", "Keel-billed Toucan", "Least Tern", "Masked Booby", "Northern Gannet", "Olive Warbler", "Painted Bunting", "Quail", "Red-cockaded Woodpecker", "Sanderling", "Toco Toucan", "Upland Sandpiper", "Varied Thrush", "White Ibis", "Xantus's Murrelet", "Yellow-billed Cuckoo- African Penguin", "Black-crowned Night Heron", "Cape Gannet", "Dalmatian Pelican", "Eurasian Spoonbill", "Flamingo", "Great Blue Heron", "Hoopoe", "Indian Peafowl", "Japanese Crane", "Kingfisher", "Lilac-breasted Roller", "Macaw", "Northern Fulmar", "Ostrich", "Puffin", "Quail", "Red-billed Tropicbird", "Secretary Bird", "Toucan", "Umbrella Cockatoo", "Vulture", "White Stork", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zebra Finch", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Double-crested Cormorant", "Eastern Bluebird", "Ferruginous Hawk", "Green Heron", "Harpy Eagle", "Indigo Bunting", "Japanese Quail", "King Rail", "Least Bittern", "Magnificent Frigatebird", "Northern Bobwhite", "Osprey", "Painted Bunting", "Quaker Parrot", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird- American Coot", "Black-capped Chickadee", "Carolina Wren", "Downy Woodpecker", "Eastern Meadowlark", "Ferruginous Hawk", "Great Crested Flycatcher", "Hooded Merganser", "Indigo Bunting", "Juniper Titmouse", "Killdeer", "Least Sandpiper", "Mountain Bluebird", "Northern Flicker", "Orchard Oriole", "Painted Bunting", "Quaker Parrot", "Red-breasted Nuthatch", "Snowy Egret", "Tufted Puffin", "Varied Thrush", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker", "American Goldfinch", "Black-crowned Night Heron", "Cedar Waxwing", "Dark-eyed Junco", "Eastern Screech Owl", "Field Sparrow", "Great Horned Owl", "House Finch", "Indigo Bunting", "Kentucky Warbler", "Least Tern", "Mourning Dove", "Northern Goshawk", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-bellied Woodpecker", "Snowy Owl", "Turkey Vulture", "Veery", "Western Grebe", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zone-tailed Hawk- American Redstart", "Black-throated Sparrow", "Cape May Warbler", "Dickcissel", "Eastern Whip-poor-will", "Ferruginous Hawk", "Great Egret", "Hooded Warbler", "Ivory-billed Woodpecker", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Gannet", "Orchard Oriole", "Painted Bunting", "Quaker Parrot", "Red-breasted Merganser", "Snowy Owl", "Tufted Puffin", "Varied Thrush", "Western Tanager", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "American Goldfinch", "Black-crowned Night Heron", "Chestnut-sided Warbler", "Dark-eyed Junco", "Eastern Screech Owl", "Field Sparrow", "Great Horned Owl", "House Finch", "Indigo Bunting", "Kentucky Warbler", "Least Tern", "Mourning Dove", "Northern Goshawk", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-bellied Woodpecker", "Snowy Owl", "Turkey Vulture", "Veery", "Western Grebe", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zone-tailed Hawk", "American Redstart", "Black-throated Sparrow- American Wigeon", "Black-throated Blue Warbler", "Cape May Warbler", "Dickcissel", "Eastern Whip-poor-will", "Ferruginous Hawk", "Great Egret", "Hooded Warbler", "Ivory-billed Woodpecker", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Gannet", "Orchard Oriole", "Painted Bunting", "Quaker Parrot", "Red-breasted Merganser", "Snowy Owl", "Tufted Puffin", "Varied Thrush", "Western Tanager", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "American Goldfinch", "Black-crowned Night Heron", "Chestnut-sided Warbler", "Dark-eyed Junco", "Eastern Screech Owl", "Field Sparrow", "Great Horned Owl", "House Finch", "Indigo Bunting", "Kentucky Warbler", "Least Tern", "Mourning Dove", "Northern Goshawk", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-bellied Woodpecker", "Snowy Owl", "Turkey Vulture", "Veery", "Western Grebe", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zone-tailed Hawk", "American Redstart", "Black-throated Sparrow- American Bittern", "Black-crowned Night Heron", "Cape May Warbler", "Dickcissel", "Eastern Whip-poor-will", "Ferruginous Hawk", "Great Egret", "Hooded Warbler", "Ivory-billed Woodpecker", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Gannet", "Orchard Oriole", "Painted Bunting", "Quaker Parrot", "Red-breasted Merganser", "Snowy Owl", "Tufted Puffin", "Varied Thrush", "Western Tanager", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "American Goldfinch", "Black-crowned Night Heron", "Chestnut-sided Warbler", "Dark-eyed Junco", "Eastern Screech Owl", "Field Sparrow", "Great Horned Owl", "House Finch", "Indigo Bunting", "Kentucky Warbler", "Least Tern", "Mourning Dove", "Northern Goshawk", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-bellied Woodpecker", "Snowy Owl", "Turkey Vulture", "Veery", "Western Grebe", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zone-tailed Hawk", "American Redstart", "Black-throated Sparrow"]} -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/cub100_ID_gpt-3.5-turbo-16k_2.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian 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"Upland Sandpiper", "Vireo", "Wagtail", "Yellowhammer- American Redstart", "Black-and-white Warbler", "Cape May Warbler", "Eastern Meadowlark", "Gray Catbird", "Hooded Warbler", "Indigo Bunting", "Kentucky Warbler", "Louisiana Waterthrush", "Mourning Warbler", "Northern Parula", "Orchard Oriole", "Prothonotary Warbler", "Red-breasted Nuthatch", "Scarlet Tanager", "Tennessee Warbler", "Veery", "White-eyed Vireo", "Yellow Warbler", "American Woodcock", "Black-billed Cuckoo", "Carolina Wren", "Eastern Screech-Owl", "Gray-cheeked Thrush", "Hermit Thrush", "Least Flycatcher", "Northern Saw-whet Owl", "Ovenbird", "Philadelphia Vireo", "Red-eyed Vireo", "Swainson's Thrush", "Warbling Vireo", "Yellow-bellied Flycatcher", "American Pipit", "Black-throated Green Warbler", "Chestnut-sided Warbler", "Eastern Wood-Pewee", "Golden-winged Warbler", "Least Tern", "Northern Waterthrush", "Palm Warbler", "Red-headed Woodpecker", "Semipalmated Sandpiper", "Western Tanager", "Yellow-billed Cuckoo", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin- American Redstart", "Black-and-white Warbler", "Cape May Warbler", "Eastern Meadowlark", "Gray Catbird", "Hooded Warbler", "Indigo Bunting", "Kentucky Warbler", "Louisiana Waterthrush", "Mourning Warbler", "Northern Parula", "Orchard Oriole", "Prothonotary Warbler", "Red-breasted Nuthatch", "Scarlet Tanager", "Tennessee Warbler", "Veery", "White-eyed Vireo", "Yellow Warbler", "American Woodcock", "Black-billed Cuckoo", "Carolina Wren", "Eastern Screech-Owl", "Gray-cheeked Thrush", "Hermit Thrush", "Least Flycatcher", "Northern Saw-whet Owl", "Ovenbird", "Philadelphia Vireo", "Red-eyed Vireo", "Swainson's Thrush", "Warbling Vireo", "Yellow-bellied Flycatcher", "American Pipit", "Black-throated Green Warbler", "Chestnut-sided Warbler", "Eastern Wood-Pewee", "Golden-winged Warbler", "Least Tern", "Northern Waterthrush", "Palm Warbler", "Red-headed Woodpecker", "Semipalmated Sandpiper", "Western Tanager", "Yellow-billed Cuckoo", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin- American Redstart", "Black-and-white Warbler", "Cape May Warbler", "Eastern Meadowlark", "Gray Catbird", "Hooded Warbler", "Indigo Bunting", "Kentucky Warbler", "Louisiana Waterthrush", "Mourning Warbler", "Northern Parula", "Orchard Oriole", "Prothonotary Warbler", "Red-breasted Nuthatch", "Scarlet Tanager", "Tennessee Warbler", "Veery", "White-eyed Vireo", "Yellow Warbler", "American Woodcock", "Black-billed Cuckoo", "Carolina Wren", "Eastern Screech-Owl", "Gray-cheeked Thrush", "Hermit Thrush", "Least Flycatcher", "Northern Saw-whet Owl", "Ovenbird", "Philadelphia Vireo", "Red-eyed Vireo", "Swainson's Thrush", "Warbling Vireo", "Yellow-bellied Flycatcher", "American Pipit", "Black-throated Green Warbler", "Chestnut-sided Warbler", "Eastern Wood-Pewee", "Golden-winged Warbler", "Least Tern", "Northern Waterthrush", "Palm Warbler", "Red-headed Woodpecker", "Semipalmated Sandpiper", "Western Tanager", "Yellow-billed Cuckoo", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin- American Redstart", "Black-and-white Warbler", "Cape May Warbler", "Eastern Meadowlark", "Gray Catbird", "Hooded Warbler", "Indigo Bunting", "Kentucky Warbler", "Louisiana Waterthrush", "Mourning Warbler", "Northern Parula", "Orchard Oriole", "Prothonotary Warbler", "Red-breasted Nuthatch", "Scarlet Tanager", "Tennessee Warbler", "Veery", "White-eyed Vireo", "Yellow Warbler", "American Woodcock", "Black-billed Cuckoo", "Carolina Wren", "Eastern Screech-Owl", "Gray-cheeked Thrush", "Hermit Thrush", "Least Flycatcher", "Northern Saw-whet Owl", "Ovenbird", "Philadelphia Vireo", "Red-eyed Vireo", "Swainson's Thrush", "Warbling Vireo", "Yellow-bellied Flycatcher", "American Pipit", "Black-throated Green Warbler", "Chestnut-sided Warbler", "Eastern Wood-Pewee", "Golden-winged Warbler", "Least Tern", "Northern Waterthrush", "Palm Warbler", "Red-headed Woodpecker", "Semipalmated Sandpiper", "Western Tanager", "Yellow-billed Cuckoo", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin"]} -------------------------------------------------------------------------------- /eval_ood_detection.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.environ['CURL_CA_BUNDLE'] = '' # for SSLError: HTTPSConnectionPool 3 | import argparse 4 | import numpy as np 5 | import torch 6 | from scipy import stats 7 | from utils.common import setup_seed, get_num_cls, get_test_labels 8 | from utils.detection_util import print_measures, get_and_print_results, get_ood_scores_clip 9 | from utils.file_ops import save_as_dataframe, setup_log 10 | from utils.plot_util import plot_distribution 11 | from utils.train_eval_util import set_model_clip, set_val_loader, set_ood_loader_ImageNet 12 | from utils.generate_llm_class import load_llm_classes 13 | from utils.args_pool import * 14 | 15 | 16 | def main(): 17 | args = process_args() 18 | setup_seed(args.seed) 19 | log = setup_log(args) 20 | assert torch.cuda.is_available() 21 | torch.cuda.set_device(args.gpu) 22 | 23 | net, preprocess = set_model_clip(args) 24 | net.eval() 25 | 26 | if args.in_dataset.startswith("ImageNet_C"): 27 | out_datasets = ['iNaturalist', 'SUN', 'places365', 'dtd'] 28 | elif args.in_dataset == 'ImageNet': 29 | if args.ood_task.startswith("far"): 30 | out_datasets = ['iNaturalist', 'SUN', 'places365', 'dtd'] 31 | else: 32 | out_datasets = ['ssb_hard', 'ninco'] 33 | else: 34 | out_datasets = dataset_mappings.get(args.in_dataset, []) 35 | 36 | 37 | test_loader = set_val_loader(args, preprocess) 38 | test_labels = get_test_labels(args, test_loader) 39 | 40 | test_labels = list(test_labels) 41 | if args.score == 'EOE': 42 | llm_labels = load_llm_classes(args, test_labels) 43 | else: 44 | llm_labels = [] 45 | 46 | print(f"test label: {test_labels}") 47 | print(f"gpt label: {llm_labels}") 48 | 49 | in_score = get_ood_scores_clip(args, net, test_loader, test_labels, llm_labels) 50 | auroc_list, aupr_list, fpr_list = [], [], [] 51 | for out_dataset in out_datasets: 52 | log.debug(f"Evaluting OOD dataset {out_dataset}") 53 | ood_loader = set_ood_loader_ImageNet(args, out_dataset, preprocess, root=args.root_dir) 54 | out_score = get_ood_scores_clip(args, net, ood_loader, test_labels, llm_labels) 55 | log.debug(f"in scores: {stats.describe(in_score)}") 56 | log.debug(f"out scores: {stats.describe(out_score)}") 57 | plot_distribution(args, in_score, out_score, out_dataset) 58 | get_and_print_results(args, log, in_score, out_score, auroc_list, aupr_list, fpr_list) 59 | log.debug('\n\nMean Test Results') 60 | print_measures(log, np.mean(auroc_list), np.mean(aupr_list), 61 | np.mean(fpr_list), method_name=args.score) 62 | save_as_dataframe(args, out_datasets, fpr_list, auroc_list, aupr_list) 63 | 64 | 65 | def process_args(): 66 | parser = argparse.ArgumentParser(description='Leverage LLMs for OOD Detection', formatter_class=argparse.ArgumentDefaultsHelpFormatter) 67 | parser.add_argument('--in_dataset', default='bird200', type=str, choices=ALL_ID_DATASET, help='in-distribution dataset') 68 | parser.add_argument('--root_dir', default="datasets", type=str, help='root dir of datasets') 69 | # prompt pipeline 70 | parser.add_argument('--ensemble', action='store_true', default=False, help='CLIP text prompt engineering') 71 | parser.add_argument('--L', type=int, default=500, help='the length of envisioned OOD class labels, for far/fine-grained: L=500, for near: L=3') 72 | parser.add_argument('--beta', type=float, default=0.25, help='beta in Eq. 3') 73 | parser.add_argument('--ood_task', type=str, default='far', choices=ALL_OOD_TASK, help='choose ood task') 74 | parser.add_argument('--generate_class', action='store_true', help='whether to envision OOD candidate classes or loaded from existing JSONs') 75 | parser.add_argument('--json_number', type=int, default=0, help='loaded json number') 76 | parser.add_argument('--llm_model', default="gpt-3.5-turbo-16k", type=str, choices=ALL_LLM, help='LLMs') 77 | parser.add_argument('--name', default="eval_ood", type=str, help="unique ID for the run") 78 | parser.add_argument('--seed', default=5, type=int, help="random seed") 79 | parser.add_argument('--gpu', default=0, type = int, help='the GPU indice to use') 80 | parser.add_argument('--batch_size', default=512, type=int, help='mini-batch size') 81 | parser.add_argument('--T', type=float, default=1, help='score temperature parameter') # It is better to set T to 0.01 for energy score in our framework 82 | parser.add_argument('--model', default='CLIP', type=str, choices=['CLIP', 'ALIGN', 'GroupViT', 'AltCLIP'], help='model architecture') 83 | parser.add_argument('--CLIP_ckpt', type=str, default='ViT-B/16', choices=['ViT-B/32', 'ViT-B/16', 'ViT-L/14'], help='which pretrained img encoder to use') 84 | parser.add_argument('--score', default='MCM', type=str, choices=['EOE', 'MCM', 'energy', 'max-logit'], help='args.score is for different comparison methods') 85 | 86 | parser.add_argument('--score_ablation', default='MAX', type=str, choices=['MAX', 'MSP', 'energy', 'max-logit', 'EOE'], help='args.score_ablation is for ablation studies in Score Functions (Sec. 4.3), i.e., the score function below will use the candidate OOD class names') 87 | parser.add_argument('--feat_dim', type=int, default=512, help='feat dim, 512 for ViT-B and 768 for ViT-L') 88 | args = parser.parse_args() 89 | 90 | args.n_cls = get_num_cls(args) 91 | args.log_directory = f"results/{args.in_dataset}/{args.score}/{args.model}_{args.CLIP_ckpt}_T_{args.T}_ID_{args.name}" 92 | 93 | os.makedirs(args.log_directory, exist_ok=True) 94 | 95 | return args 96 | 97 | 98 | if __name__ == '__main__': 99 | main() -------------------------------------------------------------------------------- /envisioned_classes/fine_grained_300/cub100_ID_gpt-3.5-turbo-16k_1.json: -------------------------------------------------------------------------------- 1 | {"fine_grained": ["American Avocet", "Black Skimmer", "California Condor", "Crested Caracara", "Eurasian Collared Dove", "Ferruginous Hawk", "Great Horned Owl", "Harpy Eagle", "Inca Tern", "King Vulture", "Laughing Kookaburra", "Marabou Stork", "Northern Cardinal", "Osprey", "Peregrine Falcon", "Quetzal", "Red-tailed Hawk", "Snowy Owl", "Tufted Puffin", "Violet-crowned Hummingbird", "White-tailed Kite", "Xantus's Hummingbird", "Yellow-headed Blackbird", "Zebra Dove", "African Grey Parrot", "Blue-footed Booby", "Cactus Wren", "Dusky Grouse", "Elegant Trogon", "Flame-colored Tanager", "Gilded Flicker", "Hawaiian Goose", "Ivory-billed Woodpecker", "Jamaican Tody", "Keel-billed Toucan", "Least Tern", "Masked Booby", "Northern Gannet", "Olive Warbler", "Painted Bunting", "Quail", "Red-cockaded Woodpecker", "Sanderling", "Toco Toucan", "Upland Sandpiper", "Varied Thrush", "White Ibis", "Xantus's Murrelet", "Yellow-billed Cuckoo- African Penguin", "Black-crowned Night Heron", "Cape Gannet", "Dalmatian Pelican", "Eurasian Spoonbill", "Flamingo", "Great Blue Heron", "Hoopoe", "Indian Peafowl", "Japanese Crane", "Kingfisher", "Lilac-breasted Roller", "Macaw", "Northern Fulmar", "Ostrich", "Puffin", "Quail", "Red-billed Tropicbird", "Secretary Bird", "Toucan", "Umbrellabird", "Vulture", "White Stork", "Xantus's Murrelet", "Yellow-billed Cuckoo", "Zebra Finch", "American Bittern", "Black-necked Stilt", "Cattle Egret", "Dunlin", "European Robin", "Frigatebird", "Gull", "Harrier", "Ibis", "Jacana", "Kestrel", "Lark", "Moorhen", "Nightjar", "Osprey", "Pheasant", "Quetzal", "Rail", "Sandpiper", "Tern", "Upland Sandpiper", "Vireo", "Warbler", "Xantus's Hummingbird", "Yellowthroat- American Coot", "Black-throated Sparrow", "California Gull", "Dusky Flycatcher", "Eastern Bluebird", "Ferruginous Hawk", "Green Heron", "Hooded Merganser", "Indigo Bunting", "Juniper Titmouse", "Killdeer", "Least Sandpiper", "Mountain Bluebird", "Northern Flicker", "Orchard Oriole", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Sanderling", "Tufted Titmouse", "Upland Sandpiper", "Vermilion Flycatcher", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker", "Zone-tailed Hawk", "American Goldfinch", "Black-capped Chickadee", "Carolina Wren", "Downy Woodpecker", "Eastern Towhee", "Field Sparrow", "Gray Catbird", "House Finch", "Indigo Bunting", "Juniper Titmouse", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Cardinal", "Orchard Oriole", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Sanderling", "Tufted Titmouse", "Upland Sandpiper", "Vermilion Flycatcher", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker- American Redstart", "Black-throated Green Warbler", "Cape May Warbler", "Dickcissel", "Eastern Meadowlark", "Fox Sparrow", "Golden-crowned Kinglet", "Hermit Thrush", "Ivory-billed Woodpecker", "Juniper Titmouse", "Kentucky Warbler", "Lazuli Bunting", "MacGillivray's Warbler", "Nashville Warbler", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Swainson's Thrush", "Townsend's Warbler", "Upland Sandpiper", "Varied Thrush", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "Zone-tailed Hawk", "American Goldfinch", "Black-capped Chickadee", "Carolina Wren", "Downy Woodpecker", "Eastern Towhee", "Field Sparrow", "Gray Catbird", "House Finch", "Indigo Bunting", "Juniper Titmouse", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Cardinal", "Orchard Oriole", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Sanderling", "Tufted Titmouse", "Upland Sandpiper", "Vermilion Flycatcher", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker- American Redstart", "Black-throated Green Warbler", "Cape May Warbler", "Dickcissel", "Eastern Meadowlark", "Fox Sparrow", "Golden-crowned Kinglet", "Hermit Thrush", "Ivory-billed Woodpecker", "Juniper Titmouse", "Kentucky Warbler", "Lazuli Bunting", "MacGillivray's Warbler", "Nashville Warbler", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Swainson's Thrush", "Townsend's Warbler", "Upland Sandpiper", "Varied Thrush", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "Zone-tailed Hawk", "American Goldfinch", "Black-capped Chickadee", "Carolina Wren", "Downy Woodpecker", "Eastern Towhee", "Field Sparrow", "Gray Catbird", "House Finch", "Indigo Bunting", "Juniper Titmouse", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Cardinal", "Orchard Oriole", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Sanderling", "Tufted Titmouse", "Upland Sandpiper", "Vermilion Flycatcher", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker- American Redstart", "Black-throated Green Warbler", "Cape May Warbler", "Dickcissel", "Eastern Meadowlark", "Fox Sparrow", "Golden-crowned Kinglet", "Hermit Thrush", "Ivory-billed Woodpecker", "Juniper Titmouse", "Kentucky Warbler", "Lazuli Bunting", "MacGillivray's Warbler", "Nashville Warbler", "Olive-sided Flycatcher", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Swainson's Thrush", "Townsend's Warbler", "Upland Sandpiper", "Varied Thrush", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Flycatcher", "Zone-tailed Hawk", "American Goldfinch", "Black-capped Chickadee", "Carolina Wren", "Downy Woodpecker", "Eastern Towhee", "Field Sparrow", "Gray Catbird", "House Finch", "Indigo Bunting", "Juniper Titmouse", "Kentucky Warbler", "Least Flycatcher", "Mountain Bluebird", "Northern Cardinal", "Orchard Oriole", "Painted Bunting", "Quail", "Red-breasted Nuthatch", "Sanderling", "Tufted Titmouse", "Upland Sandpiper", "Vermilion Flycatcher", "Western Bluebird", "Xantus's Hummingbird", "Yellow-bellied Sapsucker"]} --------------------------------------------------------------------------------