[Upload] Upload files

README.md

@@ -0,0 +1,17 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: annotation
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 8369374
+    num_examples: 10
+  - name: validation
+    num_bytes: 8369374
+    num_examples: 10
+  download_size: 8353015
+  dataset_size: 16738748
+---

girls_group.py

@@ -0,0 +1,111 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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 csv
+import json
+import os
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {K-pop girls groups dataset},
+author={smwoo, Inc.
+},
+year={2023}
+}
+"""
+
+_BASE_URL = "https://drive.google.com/u/0/uc?id=16_GUeJcC88LrB8zJni0-XdjdL14WXOVW&export=download"
+
+_METADATA_URLS = {
+    "train": "https://drive.google.com/u/0/uc?id=1mPwH_p1-QQtY0xsFjLIzQLkAXDNyB8sO&export=download",
+    "test": "https://drive.google.com/u/0/uc?id=1mPwH_p1-QQtY0xsFjLIzQLkAXDNyB8sO&export=download"
+}
+
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to learn how to make custom dataset. 
+"""
+
+_HOMEPAGE = "https://cislab.cau.ac.kr/"
+
+_LICENSE = ""
+
+_IMAGES_DIR = "data/"
+
+class GirlsGroupsConfig(datasets.BuilderConfig):
+    """BuilderConfig for GirlsGroups dataset."""
+    
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        super(GirlsGroupsConfig, self).__init__(version=datasets.Version("1.1.0"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class GirlsGroups(datasets.GeneratorBasedBuilder):
+    """GirlsGroups Images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "annotation": datasets.Value(dtype='string'),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS 
+        archive_path = dl_manager.download(_BASE_URL)
+        split_metadata_paths = dl_manager.download(_METADATA_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "metadata_path": split_metadata_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                    "metadata_path": split_metadata_paths["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        """Generate images and labels for splits."""
+        save_list = {}
+        with open(metadata_path, newline='') as csvfile:
+            spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
+            for row in spamreader:
+                save_list[row[0]] = row[1]
+        for file_path, file_obj in images:
+            yield file_path, {
+                "image": {"path": file_path, "bytes": file_obj.read()},
+                "annotation" : save_list[file_path]
+            }