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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: float() argument must be a string or a number, not 'list'
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1625, in _prepare_split_single
example = self.info.features.encode_example(record) if self.info.features is not None else record
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2041, in encode_example
return encode_nested_example(self, example)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1285, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1285, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1285, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1285, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1355, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 533, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'list'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1487, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
json
dict | mask.png
image | png
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COSOCO: Compromised Software Containers Image Dataset
COSOCO (Compromised Software Containers) is a synthetic dataset of 3364 images representing benign and malware-compromised software containers. Each image in the dataset represents a dockerized software container that has been converted to an image using common byte-to-pixel tools widely used in malware analysis. Software container records are labelled (1) benign or (2) compromised: A benign software container will have installed commonly used harmless packages and tools, whereas a compromised software container, will have, among harmless benign tools and packages, its underlying file system affected by some activated malware instance. Each compromised instance is accompanied by a mask, i.e. a black and white image which marks the pixels that correspond to the files of the underlying system that have been altered by a malware.
COSOCO aims to support the identification of compromised software containers via the task of image classification task and the identification of compromised files and file system regions inside a container via the image segmentation task.
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