Update parquet files

README.md

@@ -1,263 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-nc-nd-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- extended|other-wider
-task_categories:
-- object-detection
-task_ids:
-- face-detection
-paperswithcode_id: wider-face-1
-pretty_name: WIDER FACE
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: faces
-    sequence:
-    - name: bbox
-      sequence: float32
-      length: 4
-    - name: blur
-      dtype:
-        class_label:
-          names:
-            0: clear
-            1: normal
-            2: heavy
-    - name: expression
-      dtype:
-        class_label:
-          names:
-            0: typical
-            1: exaggerate
-    - name: illumination
-      dtype:
-        class_label:
-          names:
-            0: normal
-            1: 'exaggerate '
-    - name: occlusion
-      dtype:
-        class_label:
-          names:
-            0: 'no'
-            1: partial
-            2: heavy
-    - name: pose
-      dtype:
-        class_label:
-          names:
-            0: typical
-            1: atypical
-    - name: invalid
-      dtype: bool
-  splits:
-  - name: train
-    num_bytes: 12049881
-    num_examples: 12880
-  - name: test
-    num_bytes: 3761103
-    num_examples: 16097
-  - name: validation
-    num_bytes: 2998735
-    num_examples: 3226
-  download_size: 3676086479
-  dataset_size: 18809719
----
-
-# Dataset Card for WIDER FACE
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** http://shuoyang1213.me/WIDERFACE/index.html
-- **Repository:**
-- **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
-- **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html
-- **Point of Contact:** shuoyang.1213@gmail.com
-
-### Dataset Summary
-
-WIDER FACE dataset is a face detection benchmark dataset, of which images are
-selected from the publicly available WIDER dataset. We choose 32,203 images and
-label 393,703 faces with a high degree of variability in scale, pose and
-occlusion as depicted in the sample images. WIDER FACE dataset is organized
-based on 61 event classes. For each event class, we randomly select 40%/10%/50%
-data as training, validation and testing sets. We adopt the same evaluation
-metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
-we do not release bounding box ground truth for the test images. Users are
-required to submit final prediction files, which we shall proceed to evaluate.
-
-### Supported Tasks and Leaderboards
-
-- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html).
-
-### Languages
-
-English
-
-## Dataset Structure
-
-### Data Instances
-
-A data point comprises an image and its face annotations.
-
-```
-{
-  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': {
-    'bbox': [
-      [178.0, 238.0, 55.0, 73.0],
-      [248.0, 235.0, 59.0, 73.0],
-      [363.0, 157.0, 59.0, 73.0],
-      [468.0, 153.0, 53.0, 72.0],
-      [629.0, 110.0, 56.0, 81.0],
-      [745.0, 138.0, 55.0, 77.0]
-    ], 
-    'blur': [2, 2, 2, 2, 2, 2],
-    'expression': [0, 0, 0, 0, 0, 0],
-    'illumination': [0, 0, 0, 0, 0, 0],
-    'occlusion': [1, 2, 1, 2, 1, 2],
-    'pose': [0, 0, 0, 0, 0, 0],
-    'invalid': [False, False, False, False, False, False]
-  }
-}
-```
-
-### Data Fields
-
-- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
-- `faces`: a dictionary of face attributes for the faces present on the image
-  - `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
-  - `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy`
-  - `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1)
-  - `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1)
-  - `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2)
-  - `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1)
-  - `invalid`: whether the image is valid or invalid.
-
-### Data Splits
-
-The data is split into training, validation and testing set. WIDER FACE dataset is organized
-based on 61 event classes. For each event class, 40%/10%/50%
-data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images.
-
-## Dataset Creation
-
-### Curation Rationale
-
-The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters,
-making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
-with heavy occlusion, small scale, and atypical pose.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-WIDER FACE dataset is a subset of the WIDER dataset.
-The images in WIDER were collected in the following three steps: 1) Event categories
-were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images
-are retrieved using search engines like Google and Bing. For
-each category, 1000-3000 images were collected. 3) The
-data were cleaned by manually examining all the images
-and filtering out images without human face. Then, similar
-images in each event category were removed to ensure large
-diversity in face appearance. A total of 32203 images are
-eventually included in the WIDER FACE dataset.
-
-#### Who are the source language producers?
-
-The images are selected from publicly available WIDER dataset.
-
-### Annotations
-
-#### Annotation process
-
-The curators label the bounding boxes for all
-the recognizable faces in the WIDER FACE dataset. The
-bounding box is required to tightly contain the forehead,
-chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face
-which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating
-the face bounding boxes, they further annotate the following
-attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator
-and cross-checked by two different people.
-
-#### Who are the annotators?
-
-Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang.
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang
-
-### Licensing Information
-
-[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
-
-### Citation Information
-
-```
-@inproceedings{yang2016wider,
-    Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
-    Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-    Title = {WIDER FACE: A Face Detection Benchmark},
-    Year = {2016}}
-```
-
-### Contributions
-
-Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
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wider_face.py

@@ -1,165 +0,0 @@
-# coding=utf-8
-# 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.
-"""WIDER FACE dataset."""
-
-import os
-
-import datasets
-
-
-_HOMEPAGE = "http://shuoyang1213.me/WIDERFACE/"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"
-
-_CITATION = """\
-@inproceedings{yang2016wider,
-    Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
-    Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-    Title = {WIDER FACE: A Face Detection Benchmark},
-    Year = {2016}}
-"""
-
-_DESCRIPTION = """\
-WIDER FACE dataset is a face detection benchmark dataset, of which images are
-selected from the publicly available WIDER dataset. We choose 32,203 images and
-label 393,703 faces with a high degree of variability in scale, pose and
-occlusion as depicted in the sample images. WIDER FACE dataset is organized
-based on 61 event classes. For each event class, we randomly select 40%/10%/50%
-data as training, validation and testing sets. We adopt the same evaluation
-metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
-we do not release bounding box ground truth for the test images. Users are
-required to submit final prediction files, which we shall proceed to evaluate.
-"""
-
-
-_REPO = "https://huggingface.co/datasets/wider_face/resolve/main/data"
-_URLS = {
-    "train": f"{_REPO}/WIDER_train.zip",
-    "validation": f"{_REPO}/WIDER_val.zip",
-    "test": f"{_REPO}/WIDER_test.zip",
-    "annot": f"{_REPO}/wider_face_split.zip",
-}
-
-
-class WiderFace(datasets.GeneratorBasedBuilder):
-    """WIDER FACE dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "faces": datasets.Sequence(
-                        {
-                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-                            "blur": datasets.ClassLabel(names=["clear", "normal", "heavy"]),
-                            "expression": datasets.ClassLabel(names=["typical", "exaggerate"]),
-                            "illumination": datasets.ClassLabel(names=["normal", "exaggerate "]),
-                            "occlusion": datasets.ClassLabel(names=["no", "partial", "heavy"]),
-                            "pose": datasets.ClassLabel(names=["typical", "atypical"]),
-                            "invalid": datasets.Value("bool"),
-                        }
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "split": "train",
-                    "data_dir": data_dir["train"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "split": "test",
-                    "data_dir": data_dir["test"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "split": "val",
-                    "data_dir": data_dir["validation"],
-                    "annot_dir": data_dir["annot"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, split, data_dir, annot_dir):
-        image_dir = os.path.join(data_dir, "WIDER_" + split, "images")
-        annot_fname = "wider_face_test_filelist.txt" if split == "test" else f"wider_face_{split}_bbx_gt.txt"
-        with open(os.path.join(annot_dir, "wider_face_split", annot_fname), "r", encoding="utf-8") as f:
-            idx = 0
-            while True:
-                line = f.readline()
-                line = line.rstrip()
-                if not line.endswith(".jpg"):
-                    break
-                image_file_path = os.path.join(image_dir, line)
-                faces = []
-                if split != "test":
-                    # Read number of bounding boxes
-                    nbboxes = int(f.readline())
-                    # Cases with 0 bounding boxes, still have one line with all zeros.
-                    # So we have to read it and discard it.
-                    if nbboxes == 0:
-                        f.readline()
-                    else:
-                        for _ in range(nbboxes):
-                            line = f.readline()
-                            line = line.rstrip()
-                            line_split = line.split()
-                            assert len(line_split) == 10, f"Cannot parse line: {line_split}"
-                            line_parsed = [int(n) for n in line_split]
-                            (
-                                xmin,
-                                ymin,
-                                wbox,
-                                hbox,
-                                blur,
-                                expression,
-                                illumination,
-                                invalid,
-                                occlusion,
-                                pose,
-                            ) = line_parsed
-                            faces.append(
-                                {
-                                    "bbox": [xmin, ymin, wbox, hbox],
-                                    "blur": blur,
-                                    "expression": expression,
-                                    "illumination": illumination,
-                                    "occlusion": occlusion,
-                                    "pose": pose,
-                                    "invalid": invalid,
-                                }
-                            )
-                yield idx, {"image": image_file_path, "faces": faces}
-                idx += 1