import datasets from PIL import Image import pandas as pd from pathlib import Path class CUB200(datasets.GeneratorBasedBuilder): """Caltech-UCSD Birds-200-2011 (CUB-200-2011) Dataset""" VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( description="""The Caltech-UCSD Birds-200-2011 dataset consists of 11,788 images of 200 bird species.""", features=datasets.Features( { "image": datasets.Image(), "label": datasets.ClassLabel(names=self._labels()) } ), supervised_keys=("image", "label"), homepage="https://www.vision.caltech.edu/datasets/cub_200_2011/", citation="""@techreport{WahCUB_200_2011, Title = {The Caltech-UCSD Birds-200-2011 Dataset}, Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.}, Year = {2011}, Institution = {California Institute of Technology}, Number = {CNS-TR-2011-001}}""" ) def _split_generators(self, dl_manager): # Download and extract in a single step extracted_path = dl_manager.download_and_extract("https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz?download=1") data_dir = Path(extracted_path) / "CUB_200_2011" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir, "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"data_dir": data_dir, "split": "test"}, ) ] def _generate_examples(self, data_dir, split): """Generate examples from the extracted directory.""" # Paths to metadata files in the extracted directory image_labels_path = data_dir / "image_class_labels.txt" image_paths_path = data_dir / "images.txt" train_test_split_path = data_dir / "train_test_split.txt" # Load metadata images_df = pd.read_csv(image_paths_path, sep='\s+', header=None, names=["image_id", "file_path"]) labels_df = pd.read_csv(image_labels_path, sep='\s+', header=None, names=["image_id", "label"]) split_df = pd.read_csv(train_test_split_path, sep='\s+', header=None, names=["image_id", "is_training"]) # Merge metadata into a single DataFrame data_df = images_df.merge(labels_df, on="image_id").merge(split_df, on="image_id") data_df["label"] -= 1 # Zero-index the labels # Filter by the specified split is_training_split = 1 if split == "train" else 0 split_data = data_df[data_df["is_training"] == is_training_split] # Generate examples for _, row in split_data.iterrows(): image_path = data_dir / "images" / row['file_path'] label = row["label"] # Load the image with open(image_path, "rb") as img_file: image = Image.open(img_file).convert("RGB") yield row["image_id"], { "image": image, "label": label, } @staticmethod def _labels(): return [ "Black_footed_Albatross", "Laysan_Albatross", "Sooty_Albatross", "Groove_billed_Ani", "Crested_Auklet", "Least_Auklet", "Parakeet_Auklet", "Rhinoceros_Auklet", "Brewer_Blackbird", "Red_winged_Blackbird", "Rusty_Blackbird", "Yellow_headed_Blackbird", "Bobolink", "Indigo_Bunting", "Lazuli_Bunting", "Painted_Bunting", "Cardinal", "Spotted_Catbird", "Gray_Catbird", "Yellow_breasted_Chat", "Eastern_Towhee", "Chuck_will_Widow", "Brandt_Cormorant", "Red_faced_Cormorant", "Pelagic_Cormorant", "Bronzed_Cowbird", "Shiny_Cowbird", "Brown_Creeper", "American_Crow", "Fish_Crow", "Black_billed_Cuckoo", "Mangrove_Cuckoo", "Yellow_billed_Cuckoo", "Gray_crowned_Rosy_Finch", "Purple_Finch", "Northern_Flicker", "Acadian_Flycatcher", "Great_Crested_Flycatcher", "Least_Flycatcher", "Olive_sided_Flycatcher", "Scissor_tailed_Flycatcher", "Vermilion_Flycatcher", "Yellow_bellied_Flycatcher", "Frigatebird", "Northern_Fulmar", "Gadwall", "American_Goldfinch", "European_Goldfinch", "Boat_tailed_Grackle", "Eared_Grebe", "Horned_Grebe", "Pied_billed_Grebe", "Western_Grebe", "Blue_Grosbeak", "Evening_Grosbeak", "Pine_Grosbeak", "Rose_breasted_Grosbeak", "Pigeon_Guillemot", "California_Gull", "Glaucous_winged_Gull", "Heermann_Gull", "Herring_Gull", "Ivory_Gull", "Ring_billed_Gull", "Slaty_backed_Gull", "Western_Gull", "Anna_Hummingbird", "Ruby_throated_Hummingbird", "Rufous_Hummingbird", "Green_Violetear", "Long_tailed_Jaeger", "Pomarine_Jaeger", "Blue_Jay", "Florida_Jay", "Green_Jay", "Dark_eyed_Junco", "Tropical_Kingbird", "Gray_Kingbird", "Belted_Kingfisher", "Green_Kingfisher", "Pied_Kingfisher", "Ringed_Kingfisher", "White_breasted_Kingfisher", "Red_legged_Kittiwake", "Horned_Lark", "Pacific_Loon", "Mallard", "Western_Meadowlark", "Hooded_Merganser", "Red_breasted_Merganser", "Mockingbird", "Nighthawk", "Clark_Nutcracker", "White_breasted_Nuthatch", "Baltimore_Oriole", "Hooded_Oriole", "Orchard_Oriole", "Scott_Oriole", "Ovenbird", "Brown_Pelican", "White_Pelican", "Western_Wood_Pewee", "Sayornis", "American_Pipit", "Whip_poor_Will", "Horned_Puffin", "Common_Raven", "White_necked_Raven", "American_Redstart", "Geococcyx", "Loggerhead_Shrike", "Great_Grey_Shrike", "Baird_Sparrow", "Black_throated_Sparrow", "Brewer_Sparrow", "Chipping_Sparrow", "Clay_colored_Sparrow", "House_Sparrow", "Field_Sparrow", "Fox_Sparrow", "Grasshopper_Sparrow", "Harris_Sparrow", "Henslow_Sparrow", "Le_Conte_Sparrow", "Lincoln_Sparrow", "Nelson_Sharp_tailed_Sparrow", "Savannah_Sparrow", "Seaside_Sparrow", "Song_Sparrow", "Tree_Sparrow", "Vesper_Sparrow", "White_crowned_Sparrow", "White_throated_Sparrow", "Cape_Glossy_Starling", "Bank_Swallow", "Barn_Swallow", "Cliff_Swallow", "Tree_Swallow", "Scarlet_Tanager", "Summer_Tanager", "Arctic_Tern", "Black_Tern", "Caspian_Tern", "Common_Tern", "Elegant_Tern", "Forster_Tern", "Least_Tern", "Green_tailed_Towhee", "Brown_Thrasher", "Sage_Thrasher", "Black_capped_Vireo", "Blue_headed_Vireo", "Philadelphia_Vireo", "Red_eyed_Vireo", "Warbling_Vireo", "White_eyed_Vireo", "Yellow_throated_Vireo", "Bay_breasted_Warbler", "Black_and_white_Warbler", "Black_throated_Blue_Warbler", "Blue_winged_Warbler", "Canada_Warbler", "Cape_May_Warbler", "Cerulean_Warbler", "Chestnut_sided_Warbler", "Golden_winged_Warbler", "Hooded_Warbler", "Kentucky_Warbler", "Magnolia_Warbler", "Mourning_Warbler", "Myrtle_Warbler", "Nashville_Warbler", "Orange_crowned_Warbler", "Palm_Warbler", "Pine_Warbler", "Prairie_Warbler", "Prothonotary_Warbler", "Swainson_Warbler", "Tennessee_Warbler", "Wilson_Warbler", "Worm_eating_Warbler", "Yellow_Warbler", "Northern_Waterthrush", "Louisiana_Waterthrush", "Bohemian_Waxwing", "Cedar_Waxwing", "American_Three_toed_Woodpecker", "Pileated_Woodpecker", "Red_bellied_Woodpecker", "Red_cockaded_Woodpecker", "Red_headed_Woodpecker", "Downy_Woodpecker", "Bewick_Wren", "Cactus_Wren", "Carolina_Wren", "House_Wren", "Marsh_Wren", "Rock_Wren", "Winter_Wren", "Common_Yellowthroat" ]