refactor: script and readme
Browse files- README.md +52 -0
- selfies_and_id.pdf +0 -0
- selfies_and_id.py +118 -0
README.md
CHANGED
@@ -4,6 +4,58 @@ task_categories:
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- image-to-image
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tags:
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- code
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---
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# Selfies, ID Images dataset
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**4083** sets, which includes *2 photos of a person from his documents and 13 selfies*. **571** sets of Hispanics and **3512** sets of Caucasians.
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- image-to-image
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tags:
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- code
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dataset_info:
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features:
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- name: id_1
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dtype: image
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- name: id_2
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dtype: image
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- name: selfie_1
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dtype: image
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- name: selfie_2
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dtype: image
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- name: selfie_3
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dtype: image
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- name: selfie_4
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dtype: image
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- name: selfie_5
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dtype: image
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- name: selfie_6
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dtype: image
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- name: selfie_7
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dtype: image
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- name: selfie_8
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dtype: image
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- name: selfie_9
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dtype: image
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- name: selfie_10
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dtype: image
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- name: selfie_11
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dtype: image
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- name: selfie_12
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dtype: image
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- name: selfie_13
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dtype: image
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- name: user_id
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dtype: string
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- name: set_id
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dtype: string
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- name: user_race
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dtype: string
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- name: name
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dtype: string
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- name: age
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dtype: int8
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- name: country
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dtype: string
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- name: gender
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dtype: string
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splits:
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- name: train
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num_bytes: 376371811
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num_examples: 10
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download_size: 374658409
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dataset_size: 376371811
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---
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# Selfies, ID Images dataset
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**4083** sets, which includes *2 photos of a person from his documents and 13 selfies*. **571** sets of Hispanics and **3512** sets of Caucasians.
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selfies_and_id.pdf
ADDED
Binary file (292 kB). View file
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selfies_and_id.py
ADDED
@@ -0,0 +1,118 @@
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import io
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import datasets
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import pandas as pd
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {selfies_and_id},
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author = {TrainingDataPro},
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year = {2023}
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}
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"""
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_DESCRIPTION = """\
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4083 sets, which includes 2 photos of a person from his documents and
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13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians.
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Photo documents contains only a photo of a person.
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All personal information from the document is hidden.
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"""
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_NAME = 'selfies_and_id'
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_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
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_LICENSE = ""
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
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class SelfiesAndId(datasets.GeneratorBasedBuilder):
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"""Small sample of image-text pairs"""
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features({
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'id_1': datasets.Image(),
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'id_2': datasets.Image(),
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'selfie_1': datasets.Image(),
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'selfie_2': datasets.Image(),
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'selfie_3': datasets.Image(),
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'selfie_4': datasets.Image(),
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'selfie_5': datasets.Image(),
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'selfie_6': datasets.Image(),
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'selfie_7': datasets.Image(),
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'selfie_8': datasets.Image(),
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'selfie_9': datasets.Image(),
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'selfie_10': datasets.Image(),
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'selfie_11': datasets.Image(),
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'selfie_12': datasets.Image(),
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'selfie_13': datasets.Image(),
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'user_id': datasets.Value('string'),
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'set_id': datasets.Value('string'),
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'user_race': datasets.Value('string'),
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'name': datasets.Value('string'),
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'age': datasets.Value('int8'),
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'country': datasets.Value('string'),
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'gender': datasets.Value('string')
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}),
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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images = dl_manager.download(f"{_DATA}images.tar.gz")
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annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
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images = dl_manager.iter_archive(images)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN,
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gen_kwargs={
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"images": images,
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'annotations': annotations
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}),
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]
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def _generate_examples(self, images, annotations):
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annotations_df = pd.read_csv(annotations, sep=';')
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images_data = pd.DataFrame(columns=['URL', 'Bytes'])
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for idx, (image_path, image) in enumerate(images):
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images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()}
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annotations_df = pd.merge(annotations_df,
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images_data,
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how='left',
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on=['URL'])
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for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])):
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annotation = annotations_df.loc[annotations_df['UserId'] ==
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worker_id]
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annotation = annotation.sort_values(['FName'])
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data = {
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row[5].lower(): {
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'path': row[6],
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'bytes': row[10]
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} for row in annotation.itertuples()
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}
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age = annotation.loc[annotation['FName'] ==
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'ID_1']['Age'].values[0]
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country = annotation.loc[annotation['FName'] ==
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'ID_1']['Country'].values[0]
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gender = annotation.loc[annotation['FName'] ==
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'ID_1']['Gender'].values[0]
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set_id = annotation.loc[annotation['FName'] ==
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'ID_1']['SetId'].values[0]
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user_race = annotation.loc[annotation['FName'] ==
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'ID_1']['UserRace'].values[0]
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name = annotation.loc[annotation['FName'] ==
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'ID_1']['Name'].values[0]
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data['user_id'] = worker_id
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data['age'] = age
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data['country'] = country
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data['gender'] = gender
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data['set_id'] = set_id
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data['user_race'] = user_race
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data['name'] = name
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yield idx, data
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