Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K<n<10K
Tags:
biology
License:
File size: 2,362 Bytes
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import os
import datasets
from datasets import SplitGenerator, Split, ImageClassification
_CITATION = """\
@TECHREPORT{gpiosenka/100-bird-species,
author = {gpiosenka},
title = {BIRDS 525 SPECIES- IMAGE CLASSIFICATION},
institution = {},
year = {2023}
}
"""
_DESCRIPTION = """\
A dataset of bird species downloaded from kaggle. """
_HOMEPAGE = "https://www.kaggle.com/datasets/gpiosenka/100-bird-species/"
_DATA_DIR = 'data/'
_VERSION = "0.1.0"
def _CLASSES() -> list[str]:
# reads from bird_labels.txt, line by line
with open("birds_labels.txt") as f:
return f.read().splitlines()
class BirdSpeciesDataset(datasets.GeneratorBasedBuilder):
"""DatasetBuilder for bird_species_dataset dataset."""
DEFAULT_CONFIG_NAME = "bird_species_dataset"
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="bird_species_dataset",
version=datasets.Version(_VERSION),
description=_DESCRIPTION,
)
]
def _info(self):
_NAMES = _CLASSES()
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=ImageClassification(image_column="image", label_column="label"),
)
def _split_generators(self, dl_manager):
data_dir = _DATA_DIR
return [
SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train")}),
SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "valid")}),
SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test")}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
idx = 0
for label in os.listdir(filepath):
for f in os.listdir(os.path.join(filepath, label)):
record = {
"image": os.path.join(filepath, label, f),
"label": label,
}
yield idx, record
idx += 1
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