task_categories:image-classification size_categories:1K<n<10K source_datasets:original language:en license:odbl region:us
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.
@ONLINE {masksdata,
    author="Pool_rf",
    title="Mask face dataset",
    month="January",
    year="2023",
    url="https://huggingface.co/datasets/poolrf2001/mask"
}
---
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}
```