Update parquet files

README.md

@@ -1,16 +0,0 @@
-# FairFace (val set)
-
-Original paper: [Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf)
-
-Homepage: https://github.com/joojs/fairface
-
-Bibtex:
-```
-@inproceedings{karkkainenfairface,
-  title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
-  author={Karkkainen, Kimmo and Joo, Jungseock},
-  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
-  year={2021},
-  pages={1548--1558}
-}
-```

fairface_val_images.zip → TEST/fairface_val_padding_025-test.parquet

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-size 69269059
+oid sha256:e7c3c95f8a466bd3fde7c0e264d341ae7db22870df9e9b13c4714017d856484a
+size 63311681

fairface_val_padding_025.py

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-# coding=utf-8
-# Copyright 2022 the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import pandas as pd 
-import datasets
-import json
-from huggingface_hub import hf_hub_url
-
-_INPUT_CSV = "fairface_labeled_val.csv"
-_INPUT_IMAGES = "fairface_val_images"
-_REPO_ID = "nlphuji/fairface_val_padding_025"
-
-class Dataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="TEST", version=VERSION, description="test"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                 {
-                 "image": datasets.Image(),
-                "file": datasets.Value('string'),
-                "age": datasets.Value('string'),
-                "gender": datasets.Value('string'),
-                "race": datasets.Value('string'),
-                "service_test": datasets.Value('string'),
-                "image_name": datasets.Value('string'),
-                }
-            ),
-            task_templates=[],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        repo_id = _REPO_ID
-        data_dir = dl_manager.download_and_extract({
-            "examples_csv": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=_INPUT_CSV),
-            "images_dir": hf_hub_url(repo_id=repo_id, repo_type='dataset', filename=f"{_INPUT_IMAGES}.zip")
-        })
-
-        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
-
-
-    def _generate_examples(self, examples_csv, images_dir):
-        """Yields examples."""
-        df = pd.read_csv(examples_csv)
-
-        for r_idx, r in df.iterrows():
-            r_dict = r.to_dict()
-            image_path = os.path.join(images_dir, _INPUT_IMAGES, r_dict['image_name'])
-            r_dict['image'] = image_path
-            yield r_idx, r_dict