bird-data / bird-data.py
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Add loading script and class names.
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"""nabirds dataset."""
import os
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = "https://dl.allaboutbirds.org/nabirds"
_CITATION = """\
@MISC{Van_Horn_undated-kj,
title = "Building a bird recognition app and large scale dataset with citizen
scientists: The fine print in fine-grained dataset collection",
author = "Van Horn, Grant and Branson, Steve and Farrell, Ryan and Haber,
Scott and Barry, Jessie and Ipeirotis, Panos and Perona, Pietro and
Belongie, Serge and Lab Of Ornithology, Cornell and Tech, Cornell"
}
"""
_DESCRIPTION = """\
We worked with citizen scientists and domainexperts to collect NABirds, a new high
quality dataset containing 48,562 images of North American birds with 555
categories, part annotations and bounding boxes.
"""
_URLS = {
"train": "data/train.zip",
"test": "data/test.zip",
}
class Beans(datasets.GeneratorBasedBuilder):
"""Beans plant leaf images dataset."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names_file="classes.txt"),
}
),
supervised_keys=("image", "labels"),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=[ImageClassification(image_column="image", label_column="labels")],
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files([data_files["train"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files([data_files["test"]]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith(".jpg"):
yield i, {
"image_file_path": path,
"image": path,
"labels": os.path.basename(os.path.dirname(path)),
}