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 *.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language:
+- en
+license:
+- odbl
+pretty_name: Face Mask Detection
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- image-classification
+---
+
+## Dataset Description
+- **Homepage:** [Face Mask Detection Dataset](https://www.kaggle.com/datasets/vijaykumar1799/face-mask-detection)
+- **Repository:** N/A
+- **Paper:** N/A
+- **Leaderboard:** N/A
+- **Point of Contact:** N/A
+
+## Dataset Summary
+A dataset from [kaggle](https://www.kaggle.com/datasets/vijaykumar1799/face-mask-detection). origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data
+
+### Introduction
+
+- 
+
+### PROBLEM STATEMENT
+
+- 
+
+### About Files
+
+- Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’ which contain the images of the respective human activities.
+- Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’.
+- Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.
+- sample_submission: This is a csv file that contains the sample submission for the data sprint.
+
+### Data Fields
+The data instances have the following 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]`.
+- `labels`: an `int` classification label. All `test` data is labeled 0.
+
+### Class Label Mappings:
+```
+{
+    'mask_weared_incorrect': 0,
+    'with_mask': 1,
+    'without_mask': 2
+}
+```
+
+### Data Splits
+|               | train  | test | validation|
+|---------------|--------|------|----------:|
+| # of examples |  1500  |  180 |    180
+
+### Data Size
+
+- download: 46 MiB
+- generated: 46.8 MiB
+- total: 92.8 MiB
+
+```pycon
+>>> from datasets import load_dataset
+>>> ds = load_dataset("poolrf2001/mask")
+>>> ds
+DatasetDict({
+    test: Dataset({
+        features: ['image', 'labels'],
+        num_rows: 180
+    })
+    train: Dataset({
+        features: ['image', 'labels'],
+        num_rows: 1500
+    })
+    validation: Dataset({
+        features: ['image', 'labels'],
+        num_rows: 180
+    })
+})
+>>> ds["train"].features
+{'image': Image(decode=True, id=None),
+ 'labels': ClassLabel(num_classes=3, names=['mask_weared_incorrect', 'with_mask', 'without_mask'], id=None)}
+ 
+>>> ds["train"][0]
+{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=180x180>,
+ 'labels': 1}
+```

data_mask.json

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+{"poolrf2001--mask": {"description": "", "citation": "", "homepage": "https://www.kaggle.com/datasets/vijaykumar1799/face-mask-detection", "license": "odbl-1.0", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, 
+"labels": {"num_classes": 3, "names": ['mask_weared_incorrect', 'with_mask', 'without_mask'], "id": null, "_type": "ClassLabel"}}, 
+"post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": null, "config_name": null, "version": null, "splits": {"train": {"name": "train", "num_bytes": 49140913, "num_examples": 1500, "dataset_name": "Mask"}, 
+"test": {"name": "test", "num_bytes": 4772564, "num_examples": 180, "dataset_name": "Mask"},"validation": {"name": "validation", "num_bytes": 4848704, "num_examples": 180, "dataset_name": "Mask"}}, "download_checksums": null, "download_size": 48234496, "post_processing_size": null, "dataset_size": 49073356.8, "size_in_bytes": 97307852.8}}

mask.py

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+# -*- coding: utf-8 -*-
+"""Untitled9.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qRAN4BBFZkzQKFec3qaXZ_obxuWQa6c6
+"""
+
+# 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.
+"""Beans leaf dataset with images of diseased and health leaves."""
+
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://huggingface.co/datasets/poolrf2001/mask"
+
+_CITATION = """\
+@ONLINE {masksdata,
+    author="Pool_rf",
+    title="Mask face dataset",
+    month="January",
+    year="2023",
+    url="https://huggingface.co/datasets/poolrf2001/mask"
+}
+"""
+
+_DESCRIPTION = """\
+MaskFace es un conjunto de datos de imágenes de personas con y sin mascarillas Consta de 3 clases: 1 clase de si la persona está puesta la mascarilla, 
+otra clase si la persona no esta puesta la mascarilla y una clase donde la persona está puesta la mascarilla incorrectamente.
+"""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/train.zip",
+    "validation": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/validation.zip",
+    "test": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/test.zip",
+}
+
+_NAMES = ["mask_weared_incorrect", "width_mask", "without_mask"]
+
+
+class mask(datasets.GeneratorBasedBuilder):
+    """MaskFace images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        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.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["validation"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["test"]]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".png"):
+                yield i, {
+                    
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)).lower(),
+                }

test.zip

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+size 4693735

train.zip

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+size 38806014

validation.zip

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