Delete cats_vs_dogs_sample.py

cats_vs_dogs_sample.py

@@ -1,71 +0,0 @@
-# coding=utf-8
-# Copyright 2021 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.
-"""Sample of the Microsoft Cats vs. Dogs dataset"""
-
-from pathlib import Path
-from typing import List
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-_URL = "https://huggingface.co/datasets/hf-internal-testing/cats_vs_dogs_sample/resolve/main/cats_and_dogs_sample.zip"
-
-_HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765"
-
-_DESCRIPTION = "A 50 image sample of microsoft's cats vs. dogs dataset for unit testing."
-
-_CITATION = """\\n@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
-    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
-    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
-    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
-    year = {2007},
-    month = {October},
-    publisher = {Association for Computing Machinery, Inc.},
-    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
-    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
-}
-"""
-
-
-class CatsVsDogs(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "labels": datasets.features.ClassLabel(names=["cat", "dog"]),
-                }
-            ),
-            supervised_keys=("image", "labels"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImagesSample"
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images_path": images_path}),
-        ]
-
-    def _generate_examples(self, images_path):
-        logger.info("generating examples from = %s", images_path)
-        for i, filepath in enumerate(sorted(images_path.glob("**/*.jpg"))):
-            yield str(i), {
-                "image": str(filepath),
-                "labels": filepath.parent.name.lower(),
-            }