loading script, readme, gitignore

.gitignore

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+setup.cfg
+bin/

README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label_id
+    dtype:
+      class_label:
+        names:
+          '0': bathroom
+          '1': bedroom
+          '2': dining_room
+          '3': kitchen
+          '4': living_room
+  - name: label_name
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 810900
+    num_examples: 4000
+  - name: test
+    num_bytes: 201725
+    num_examples: 1000
+  download_size: 1888811124
+  dataset_size: 1012625
+---

interior-cgi.py

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+# 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.
+
+"""This dataset contains interior images created using DALL-E.
+The dataset contains 512x512 images split into 5 classes:
+* bathroom: 1000 images
+* bedroom: 1000 images
+* dining_room: 1000 images
+* kitchen: 1000 images
+* living_room: 1000 images
+"""
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+from datasets.tasks import ImageClassification
+from pathlib import Path
+from typing import List, Iterator
+
+_ALLOWED_IMG_EXT = {".png", ".jpg"}
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {Computer Generated interior images},
+author={Padilla, Rafael},
+year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+This new dataset contains CG interior images representing interior of houses in 5 classes, with \
+    1000 images per class.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/"
+
+_LICENSE = ""
+
+_URLS = {
+    "test": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/test.zip",
+    "train": "https://huggingface.co/datasets/rafaelpadilla/interior-cgi/resolve/main/data/train.zip",
+}
+
+_NAMES = ["bathroom", "bedroom", "dining_room", "kitchen", "living_room"]
+
+
+class CGInteriorDataset(datasets.GeneratorBasedBuilder):
+    """CGInterior: Computer Generated Interior images dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self) -> datasets.DatasetInfo:
+        """
+        Returns the dataset metadata and features.
+
+        Returns:
+            DatasetInfo: Metadata and features of the dataset.
+        """
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label_id": datasets.features.ClassLabel(names=_NAMES),
+                    "label_name": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("image", "label_id"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                ImageClassification(image_column="image", label_column="label_id")
+            ],
+        )
+
+    def _split_generators(
+        self, dl_manager: DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        """
+        Provides the split information and downloads the data.
+
+        Args:
+            dl_manager (DownloadManager): The DownloadManager to use for downloading and extracting data.
+
+        Returns:
+            List[SplitGenerator]: List of SplitGenerator objects representing the data splits.
+        """
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files(data_files["train"]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files(data_files["test"]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files: List[str]) -> Iterator:
+        """
+        Generates examples for the dataset.
+
+        Args:
+            files (List[str]): List of image paths.
+
+        Yields:
+            Dict[str, Union[str, Image]]: A dictionary containing the generated examples.
+        """
+
+        for idx, img_path in enumerate(files):
+            path = Path(img_path)
+            if path.suffix in _ALLOWED_IMG_EXT:
+                yield idx, {
+                    "image": img_path,
+                    "label_id": path.parent.name,
+                    "label_name": path.parent.name,
+                }