task_categories:object-detection annotations_creators:self language_creators:found multilinguality:monolingual size_categories:n<1K source_datasets:original language:en license:unknown food region:us
---
annotations_creators:
 - self
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
paperswithcode_id: food
pretty_name: food
tags:
- food
dataset_info:
  features:
  - name: image_id
    dtype: int64
  - name: image
    dtype: Image
  - name: width
    dtype: int32
  - name: height
    dtype: int32
  - name: objects
    sequence:
    - name: id
      dtype: int64
    - name: area
      dtype: int64
    - name: bbox
      sequence: float32
      length: 4
    - name: category
      dtype:
        class_label:
          names:
            '1': Broccoli
            '2': Tomato
            '3': Potato
---

# Dataset Card for Food

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [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)

## Dataset Description



### Dataset Summary

Sample dataset with vegetable annotations

### Supported Tasks and Leaderboards

- `object-detection`: The dataset can be used to train a model for Object Detection. 

### Languages

English

## Dataset Structure
[More Information Needed]

### Data Instances

A data point comprises an image and its object annotations.

```
{
  'image_id': 75,
  'image': Test_233.jpg,
  'width': 620,
  'height': 350,
  'objects': {
    'id': [212,213,214,215,216,217], 
    'area': [9138.402799999996,7127.4616,8837.071,7997.723299999998,7590.117599999998,8844.078],
    'bbox': [
      [245.82,165.21,95.63,95.56],
      [363.72,100.47,84.08,84.77],
      [ 154.88,99.7,91.01,97.1],
      [308.24,8.77,89.47,89.39]
    ], 
    'category': [2, 2, 2, 2]
  }
}
```

### Data Fields

- `image`: the image id
- `image`: `image name`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
  - `id`: the annotation id
  - `area`: the area of the bounding box
  - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
  - `category`: the object's category:`Broccoli` (1),`Tomato` (2),`Potato` (3)

### Data Splits

The data is not split

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

The images for this dataset were collected from Flickr and Google Images.

### Annotations

#### Annotation process

The dataset was labelled : Broccoli, Tomato, Potato

#### Who are the annotators?

Hadassah did the annotations using CVAT tool.

### 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
[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

### Contributions
