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folder/cub200_dataset.py

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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,
-                }