Convert dataset to Parquet (#5)

- Convert dataset to Parquet (8dfb0ebc19f681973bcd14712924c95961e8820f)
- Delete loading script (3147fe9de25dcf5e0d79caef7b8ab15bb120a6e2)
- Delete legacy dataset_infos.json (7c1943574cc1130f735f5f7ff1ba50231b4aa166)

README.md

@@ -50,13 +50,20 @@ dataset_info:
             '4': Mask
   splits:
   - name: train
-    num_bytes: 240481257
+    num_bytes: 240463364.0
     num_examples: 1000
   - name: test
-    num_bytes: 4172715
+    num_bytes: 4172164.0
     num_examples: 29
-  download_size: 238482705
-  dataset_size: 244653972
+  download_size: 241152653
+  dataset_size: 244635528.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for CPPE - 5

cppe-5.py

@@ -1,134 +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.
-"""CPPE-5 dataset."""
-
-
-import collections
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@misc{dagli2021cppe5,
-      title={CPPE-5: Medical Personal Protective Equipment Dataset},
-      author={Rishit Dagli and Ali Mustufa Shaikh},
-      year={2021},
-      eprint={2112.09569},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-"""
-
-_DESCRIPTION = """\
-CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
-to allow the study of subordinate categorization of medical personal protective equipments,
-which is not possible with other popular data sets that focus on broad level categories.
-"""
-
-_HOMEPAGE = "https://sites.google.com/view/cppe5"
-
-_LICENSE = "Unknown"
-
-_URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
-
-_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
-
-
-class CPPE5(datasets.GeneratorBasedBuilder):
-    """CPPE - 5 dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image_id": datasets.Value("int64"),
-                "image": datasets.Image(),
-                "width": datasets.Value("int32"),
-                "height": datasets.Value("int32"),
-                "objects": datasets.Sequence(
-                    {
-                        "id": datasets.Value("int64"),
-                        "area": datasets.Value("int64"),
-                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                        "category": datasets.ClassLabel(names=_CATEGORIES),
-                    }
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annotation_file_path": "annotations/train.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "annotation_file_path": "annotations/test.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, annotation_file_path, files):
-        def process_annot(annot, category_id_to_category):
-            return {
-                "id": annot["id"],
-                "area": annot["area"],
-                "bbox": annot["bbox"],
-                "category": category_id_to_category[annot["category_id"]],
-            }
-
-        image_id_to_image = {}
-        idx = 0
-        # This loop relies on the ordering of the files in the archive:
-        # Annotation files come first, then the images.
-        for path, f in files:
-            file_name = os.path.basename(path)
-            if path == annotation_file_path:
-                annotations = json.load(f)
-                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
-                image_id_to_annotations = collections.defaultdict(list)
-                for annot in annotations["annotations"]:
-                    image_id_to_annotations[annot["image_id"]].append(annot)
-                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
-            elif file_name in image_id_to_image:
-                image = image_id_to_image[file_name]
-                objects = [
-                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
-                ]
-                yield idx, {
-                    "image_id": image["id"],
-                    "image": {"path": path, "bytes": f.read()},
-                    "width": image["width"],
-                    "height": image["height"],
-                    "objects": objects,
-                }
-                idx += 1

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba7343d525f53dab1e84e0c5a86087960cb739e697b74d60b84bd28253e84740
+size 4137134

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f603cc9b482a529496acf0018b93590243d3cfc15b40e6e858dde8284dadd6
+size 237015519

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal\nto allow the study of subordinate categorization of medical personal protective equipments,\nwhich is not possible with other popular data sets that focus on broad level categories.\n", "citation": "@misc{dagli2021cppe5,\n      title={CPPE-5: Medical Personal Protective Equipment Dataset},\n      author={Rishit Dagli and Ali Mustufa Shaikh},\n      year={2021},\n      eprint={2112.09569},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV}\n}\n", "homepage": "https://sites.google.com/view/cppe5", "license": "Unknown", "features": {"image_id": {"dtype": "int64", "id": null, "_type": "Value"}, "image": {"id": null, "_type": "Image"}, "width": {"dtype": "int32", "id": null, "_type": "Value"}, "height": {"dtype": "int32", "id": null, "_type": "Value"}, "objects": {"feature": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "area": {"dtype": "int64", "id": null, "_type": "Value"}, "bbox": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}, "category": {"num_classes": 5, "names": ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cppe5", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 240481281, "num_examples": 1000, "dataset_name": "cppe5"}, "test": {"name": "test", "num_bytes": 4172739, "num_examples": 29, "dataset_name": "cppe5"}}, "download_checksums": {"https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr": {"num_bytes": 238482705, "checksum": "1151086e59fcb87825ecf4d362847a3f023ba69e7ace0f513d5aadc0e3dd3094"}}, "download_size": 238482705, "post_processing_size": null, "dataset_size": 244654020, "size_in_bytes": 483136725}}