Datasets:
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# -*- coding: utf-8 -*-
"""cub200_dataset.py
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1qC5RnFLP3_9X50ripGf5YtfXnugxBj2m
"""
from PIL import Image
import os
import pandas as pd
from datasets import DatasetDict, DatasetInfo, Features, Value, Sequence, Image, SplitGenerator, GeneratorBasedBuilder, Version
_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}
}
"""
_DESCRIPTION = """\
The CUB-200-2011 dataset contains 11,788 photos of 200 bird species. Each photo comes with detailed annotations, including part locations, bounding boxes, and attributes for studying fine-grained visual categorization.
"""
_HOMEPAGE = "http://www.vision.caltech.edu/visipedia/CUB-200-2011.html"
_DATASET_PATH = "/content/drive/My Drive/cub200/CUB_200_2011"
class CUB2002011(datasets.GeneratorBasedBuilder):
"""CUB-200-2011 dataset for bird species image classification."""
# Version of the dataset
VERSION = datasets.Version("1.0.0")
# Define the features of the dataset, including the image and the label
def _info(self):
return datasets.DatasetInfo(
description="CUB-200-2011 is an image dataset with photos of 200 bird species.",
features=datasets.Features({
"image": datasets.Image(),
"label": datasets.ClassLabel(names=[f"species_{i:03d}" for i in range(1, 201)]),
}),
supervised_keys=("image", "label"),
homepage="http://www.vision.caltech.edu/visipedia/CUB-200-2011.html",
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}
}"""
)
# Specify the dataset splits
def _split_generators(self, dl_manager):
# Assuming the dataset is pre-downloaded
dl_manager = DownloadManager.download_and_extract("https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz")
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"}),
]
# Generate examples from the dataset directory
def _generate_examples(self, data_dir, split):
# Implement logic to iterate over the dataset and yield examples
# For simplicity, assuming all images are in the 'images' folder and split is ignored
species_dirs = [p for p in (data_dir / "images").iterdir() if p.is_dir()]
for species_dir in species_dirs:
species_label = species_dir.name
for image_path in species_dir.glob("*.jpg"):
# The key can be whatever unique identifier you choose; here we use the image path
yield image_path.stem, {
"image": str(image_path),
"label": species_label,
} |