
# 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

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"}}}}]}]}}
TAGS
#task_categories-object-detection #annotations_creators-self #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #food #region-us 


# Dataset Card for Food

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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


### Data Instances

A data point comprises an image and its object annotations.



### 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 format)
  - 'category': the object's category:'Broccoli' (1),'Tomato' (2),'Potato' (3)

### Data Splits

The data is not split

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### 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



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators


### Licensing Information





### Contributions

# Dataset Card for Food ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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 ### Data Instances

A data point comprises an image and its object annotations. ### 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 format)
  - 'category': the object's category:'Broccoli' (1),'Tomato' (2),'Potato' (3) ### Data Splits

The data is not split ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### 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 ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#task_categories-object-detection #annotations_creators-self #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #food #region-us 
 # Dataset Card for Food ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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 ### Data Instances

A data point comprises an image and its object annotations. ### 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 format)
  - 'category': the object's category:'Broccoli' (1),'Tomato' (2),'Potato' (3) ### Data Splits

The data is not split ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### 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 ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#task_categories-object-detection #annotations_creators-self #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #food #region-us 
# Dataset Card for Food## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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### Data Instances

A data point comprises an image and its object annotations.### 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 format)
  - 'category': the object's category:'Broccoli' (1),'Tomato' (2),'Potato' (3)### Data Splits

The data is not split## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### 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## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases