metadata

annotations_creators:
  - expert-generated
  - machine-generated
language:
  - en
language_creators: []
license:
  - mit
multilinguality: []
paperswithcode_id: acronym-identification
pretty_name: Nailbiting Classification
size_categories:
  - 1K<n<10K
source_datasets:
  - original
tags:
  - nailbiting
  - image
  - preprocesses
task_categories:
  - image-classification
task_ids: []
train-eval-index:
  - col_mapping:
      labels: tags
      tokens: tokens
    config: default
    splits:
      eval_split: test
    task: token-classification
    task_id: entity_extraction
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': biting
            '1': no_biting
  splits:
    - name: train
      num_bytes: 11965731.715
      num_examples: 6629
    - name: test
      num_bytes: 1485426
      num_examples: 736
  download_size: 11546517
  dataset_size: 13451157.715

Dataset Card for Nail Biting Classification

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://huggingface.co/datasets/alecsharpie/nailbiting_classification
Repository: https://github.com/alecsharpie/nomo_nailbiting
Point of Contact: [email protected]

Dataset Summary

A binary image dataset for classifying nailbiting. Images are cropped to only show the mouth area. Should contain edge cases such as drinking water, talking on the phone, scratching chin etc.. all in "no biting" category

Dataset Structure

Data Instances

7147 Images
14879790 bytes total
12332617 bytes download

Data Fields

128 x 64 (w x h, pixels) Black and white

Labels

'0': biting
'1': no_biting

Data Splits

train: 6629 (11965737 bytes)
test: 1471 (2914053 bytes)

Dataset Creation

Curation Rationale

I wanted to create a notification system to help me stop biting my nails. It needed to contain lots of possible no-biting scenarios. eg talking on the phone

Source Data

Initial Data Collection and Normalization

The data was scraped from stock images sites and photos of myself were taken with my webcam. MTCNN (https://github.com/ipazc/mtcnn) was then used to crop the images down to only the show the mouth area The images were then converted to a black & white colour scheme.

Annotations

Annotation process

During the scraping process images were labelled with a description, which I then manually sanity checked. I labelled the ones of me manually.

Who are the annotators?

Alec Sharp

Considerations for Using the Data

Discussion of Biases & Limitations

Tried to make the dataset diverse in terms of age and skin tone. Although, this dataset contains a large number of images of one subject (me) so is biased towards lower quality webcam pictures of a white male with a short beard.

Dataset Curators