Delete selfies_and_id.py

selfies_and_id.py

@@ -1,118 +0,0 @@
-import io
-
-import datasets
-import pandas as pd
-
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {selfies_and_id},
-author = {TrainingDataPro},
-year = {2023}
-}
-"""
-
-_DESCRIPTION = """\
-4083 sets, which includes 2 photos of a person from his documents and
-13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians.
-Photo documents contains only a photo of a person.
-All personal information from the document is hidden.
-"""
-_NAME = 'selfies_and_id'
-
-_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
-
-_LICENSE = ""
-
-_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
-
-
-class SelfiesAndId(datasets.GeneratorBasedBuilder):
-    """Small sample of image-text pairs"""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                'id_1': datasets.Image(),
-                'id_2': datasets.Image(),
-                'selfie_1': datasets.Image(),
-                'selfie_2': datasets.Image(),
-                'selfie_3': datasets.Image(),
-                'selfie_4': datasets.Image(),
-                'selfie_5': datasets.Image(),
-                'selfie_6': datasets.Image(),
-                'selfie_7': datasets.Image(),
-                'selfie_8': datasets.Image(),
-                'selfie_9': datasets.Image(),
-                'selfie_10': datasets.Image(),
-                'selfie_11': datasets.Image(),
-                'selfie_12': datasets.Image(),
-                'selfie_13': datasets.Image(),
-                'user_id': datasets.Value('string'),
-                'set_id': datasets.Value('string'),
-                'user_race': datasets.Value('string'),
-                'name': datasets.Value('string'),
-                'age': datasets.Value('int8'),
-                'country': datasets.Value('string'),
-                'gender': datasets.Value('string')
-            }),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        images = dl_manager.download(f"{_DATA}images.tar.gz")
-        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        images = dl_manager.iter_archive(images)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,
-                                    gen_kwargs={
-                                        "images": images,
-                                        'annotations': annotations
-                                    }),
-        ]
-
-    def _generate_examples(self, images, annotations):
-        annotations_df = pd.read_csv(annotations, sep=';')
-        images_data = pd.DataFrame(columns=['URL', 'Bytes'])
-        for idx, (image_path, image) in enumerate(images):
-            images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()}
-
-        annotations_df = pd.merge(annotations_df,
-                                  images_data,
-                                  how='left',
-                                  on=['URL'])
-        for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])):
-            annotation = annotations_df.loc[annotations_df['UserId'] ==
-                                            worker_id]
-            annotation = annotation.sort_values(['FName'])
-            data = {
-                row[5].lower(): {
-                    'path': row[6],
-                    'bytes': row[10]
-                } for row in annotation.itertuples()
-            }
-
-            age = annotation.loc[annotation['FName'] ==
-                                 'ID_1']['Age'].values[0]
-            country = annotation.loc[annotation['FName'] ==
-                                     'ID_1']['Country'].values[0]
-            gender = annotation.loc[annotation['FName'] ==
-                                    'ID_1']['Gender'].values[0]
-            set_id = annotation.loc[annotation['FName'] ==
-                                    'ID_1']['SetId'].values[0]
-            user_race = annotation.loc[annotation['FName'] ==
-                                       'ID_1']['UserRace'].values[0]
-            name = annotation.loc[annotation['FName'] ==
-                                  'ID_1']['Name'].values[0]
-
-            data['user_id'] = worker_id
-            data['age'] = age
-            data['country'] = country
-            data['gender'] = gender
-            data['set_id'] = set_id
-            data['user_race'] = user_race
-            data['name'] = name
-
-            yield idx, data