---
annotations_creators:
- found
language_creators:
- found
language:
- en
license: mit
multilinguality: monolingual
size_categories:
- 100<n<1K
source_datasets:
- original
task_categories:
- image-classification
pretty_name: ImageDataHW1
---

# Dataset Card for ImageDataHW1

<!-- This dataset has image data of fencing competition, and is meant for classifying whether or not a point has been awarded.
 -->
This dataset has image data of fencing competition, and is meant for classifying whether or not a point has been awarded

## Dataset Details

### Dataset Description

The original split contains thirty screenshots retrieved from the source below. 15 images of the original split show frames where a point has not yet been 
awarded, while 15 show frames where a point has been awarded. The augmented split shows 300 images that have been augmented via torchvision transforms.

- **Curated by:** Ethan Kessler

- **License:** MIT

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

this dataset is made for image classification training

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

More Information Needed

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The original split contains thirty screenshots retrieved from the source below.
15 images of the original split show frames where a point has not yet been awarded, while 15 show frames where a point has been awarded.
The augmented split shows 300 images that have been augmented via torchvision transforms.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

More Information Needed

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

More Information Needed

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

original split screenshots retrieved from https://actions.quarte-riposte.com/

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Labeling process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

the original split data was labelled manually based on the scorebug monitor from the screenshots

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

More Information Needed

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

More Information Needed

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Dataset Card Contact

More Information Needed