Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: AttributeError
Message: 'str' object has no attribute 'items'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, 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 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
return HubDatasetModuleFactory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 681, in get_module
{
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 682, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 170, in __post_init__
self.features = Features.from_dict(self.features)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1872, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
AttributeError: 'str' object has no attribute 'items'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 Description
This dataset is for image classification, featuring images of the cartoon characters Tom and Jerry. It was collected from a Kaggle competition and organized into four distinct classes: Jerry, Tom, Tom and Jerry, and Neither Tom nor Jerry.
Homepage: https://www.kaggle.com/datasets/balabaskar/tom-and-jerry-image-classification
Repository: https://huggingface.co/datasets/agueror/tom-and-jerry-v1
Dataset Structure
The dataset consists of three splits: train, validation, and test, with each split stored as a JSON Lines (.jsonl) file. The images themselves are organized in a separate folder.
agueror/tom-and-jerry-v1/
├── train.jsonl
├── validation.jsonl
├── test.jsonl
├── Jerry/
├── Tom/
├── Tom and Jerry/
└── Neither Tom nor Jerry/
image_path: The relative file path to the image. label: The classification label for the image.
How to use it?
from datasets import load_dataset, Image, ClassLabel
# Load the dataset from Hugging Face Hub
dataset = load_dataset(
"json",
data_files={
"train": "https://huggingface.co/datasets/agueror/tom-and-jerry-v1/resolve/main/train.jsonl",
"validation": "https://huggingface.co/datasets/agueror/tom-and-jerry-v1/resolve/main/validation.jsonl",
"test": "https://huggingface.co/datasets/agueror/tom-and-jerry-v1/resolve/main/test.jsonl"
}
)
# Cast the 'label' and 'image_path' columns
dataset = dataset.cast_column('image_path', Image())
dataset = dataset.cast_column('label', ClassLabel(names=['jerry', 'tom', 'tom_jerry_0', 'tom_jerry_1']))
# Access the training split
print(dataset["train"][0])
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