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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
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