Upload cub200_dataset.py

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,
+                }