Error code:   ConfigNamesError
Exception:    ValueError
Message:      ClassLabel expected a value for all label ids [0:4] but some ids are missing.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 56, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1492, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1469, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1028, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1837, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1833, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1833, in <dictcomp>
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1813, in from_yaml_inner
                  return {"feature": from_yaml_inner(_feature), **obj, "_type": "Sequence"}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1833, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1833, in <dictcomp>
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1828, in from_yaml_inner
                  return from_yaml_inner(obj["dtype"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1830, in from_yaml_inner
                  return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1800, in unsimplify
                  raise ValueError(
              ValueError: ClassLabel expected a value for all label ids [0:4] but some ids are missing.

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Dataset Card for Food

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 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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