Create country211.py

country211.py

@@ -0,0 +1,105 @@
+import tarfile
+from tqdm import tqdm
+import datasets
+from PIL import Image
+import io
+
+
+class Country211(datasets.GeneratorBasedBuilder):
+    """Country211: Image Classification Dataset for Geolocation.
+    This dataset uses a subset of the YFCC100M dataset, filtered by GPS coordinates to include images labeled
+    with ISO-3166 country codes. Each country has a balanced sample of images for training, validation, and testing.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Country211 dataset for image classification by country.",
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=self._class_names())
+            }),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/openai/CLIP/blob/main/data/country211.md",
+            citation="""@inproceedings{radford2021learning,
+                         title={Learning transferable visual models from natural language supervision},
+                         author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
+                         booktitle={International conference on machine learning},
+                         pages={8748--8763},
+                         year={2021},
+                         organization={PMLR}}"""
+        )
+
+    def _split_generators(self, dl_manager):
+        # Define download URL and local path
+        urls = "https://openaipublic.azureedge.net/clip/data/country211.tgz"
+        archive_path = dl_manager.download(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"archive_path": archive_path, "split": "valid"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"archive_path": archive_path, "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, split):
+        """Generate examples from the tar archive."""
+        with tarfile.open(archive_path, "r:gz") as archive:
+            # Navigate to the relevant split directory within the archive
+            split_dir = f"country211/{split}"
+
+            # Get the class names
+            class_names = self._class_names()
+            class_to_idx = {name: idx for idx, name in enumerate(class_names)}
+
+            # Initialize a counter for unique IDs
+            idx = 0
+
+            for member in tqdm(archive.getmembers(), desc=f"Processing {split} split"):
+                # Only process files within the specific split directory
+                if member.isfile() and member.name.startswith(split_dir):
+                    # Extract the country code from the directory name
+                    path_parts = member.name.split("/")
+                    country_code = path_parts[2]
+
+                    # Check if the country code is valid
+                    if country_code in class_to_idx:
+                        label = class_to_idx[country_code]
+
+                        # Extract and open the image
+                        with archive.extractfile(member) as file:
+                            image = Image.open(io.BytesIO(file.read())).convert("RGB")
+
+                            # Use the counter as the unique ID
+                            yield idx, {
+                                "image": image,
+                                "label": label,
+                            }
+                            idx += 1  # Increment the counter for the next image
+                    else:
+                        raise ValueError(f"Invalid country code: {country_code}")
+
+    @staticmethod
+    def _class_names():
+        return ['AD', 'AE', 'AF', 'AG', 'AI', 'AL', 'AM', 'AO', 'AQ', 'AR', 'AT', 'AU', 'AW', 'AX', 'AZ', 'BA', 'BB',
+                'BD', 'BE', 'BF', 'BG', 'BH', 'BJ', 'BM', 'BN', 'BO', 'BQ', 'BR', 'BS', 'BT', 'BW', 'BY', 'BZ', 'CA',
+                'CD', 'CF', 'CH', 'CI', 'CK', 'CL', 'CM', 'CN', 'CO', 'CR', 'CU', 'CV', 'CW', 'CY', 'CZ', 'DE', 'DK',
+                'DM', 'DO', 'DZ', 'EC', 'EE', 'EG', 'ES', 'ET', 'FI', 'FJ', 'FK', 'FO', 'FR', 'GA', 'GB', 'GD', 'GE',
+                'GF', 'GG', 'GH', 'GI', 'GL', 'GM', 'GP', 'GR', 'GS', 'GT', 'GU', 'GY', 'HK', 'HN', 'HR', 'HT', 'HU',
+                'ID', 'IE', 'IL', 'IM', 'IN', 'IQ', 'IR', 'IS', 'IT', 'JE', 'JM', 'JO', 'JP', 'KE', 'KG', 'KH', 'KN',
+                'KP', 'KR', 'KW', 'KY', 'KZ', 'LA', 'LB', 'LC', 'LI', 'LK', 'LR', 'LT', 'LU', 'LV', 'LY', 'MA', 'MC',
+                'MD', 'ME', 'MF', 'MG', 'MK', 'ML', 'MM', 'MN', 'MO', 'MQ', 'MR', 'MT', 'MU', 'MV', 'MW', 'MX', 'MY',
+                'MZ', 'NA', 'NC', 'NG', 'NI', 'NL', 'NO', 'NP', 'NZ', 'OM', 'PA', 'PE', 'PF', 'PG', 'PH', 'PK', 'PL',
+                'PR', 'PS', 'PT', 'PW', 'PY', 'QA', 'RE', 'RO', 'RS', 'RU', 'RW', 'SA', 'SB', 'SC', 'SD', 'SE', 'SG',
+                'SH', 'SI', 'SJ', 'SK', 'SL', 'SM', 'SN', 'SO', 'SS', 'SV', 'SX', 'SY', 'SZ', 'TG', 'TH', 'TJ', 'TL',
+                'TM', 'TN', 'TO', 'TR', 'TT', 'TW', 'TZ', 'UA', 'UG', 'US', 'UY', 'UZ', 'VA', 'VE', 'VG', 'VI', 'VN',
+                'VU', 'WS', 'XK', 'YE', 'ZA', 'ZM', 'ZW']