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Cannot get the config names for the dataset.
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 79, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1237, 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 464, 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 395, 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 1918, 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 1914, 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 1914, 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 1894, 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 1914, 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 1914, 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 1909, 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 1911, 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 1881, in unsimplify
raise ValueError(
ValueError: ClassLabel expected a value for all label ids [0:4] but some ids are missing.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 idimage:image namewidth: the image widthheight: the image heightobjects: a dictionary containing bounding box metadata for the objects present on the imageid: the annotation idarea: the area of the bounding boxbbox: 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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