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Error code: DatasetGenerationError
Exception: ValueError
Message: Invalid string class label Brain_Tumor_MRI_4c_Aug_Split@17c3d22cf3b2222c329fad05f9add875c774867d
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1537, in _prepare_split_single
example = self.info.features.encode_example(record) if self.info.features is not None else record
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label Brain_Tumor_MRI_4c_Aug_Split@17c3d22cf3b2222c329fad05f9add875c774867d
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, 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.
image image | label class label |
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0glioma_tumor | |
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0glioma_tumor |
- language: en
tags:
- brain cancer
- image classification
- glioma
- meningioma
- pituitary tumor
- medical imaging
- Random Contrast Learning
- PrismRCL
license: mit
datasets:
- brain-cancer-4class
- Overview
- Dataset Structure
- Features
- Usage (pre-split; optimal parameters)
- License
- Original Source
- Additional Information
language: en tags: - brain cancer - image classification - glioma - meningioma - pituitary tumor - medical imaging - Random Contrast Learning - PrismRCL license: mit datasets: - brain-cancer-4class
Brain Cancer 4-Class Image Dataset
Overview
This dataset contains MRI images for classifying brain tumors across four categories: glioma, meningioma, pituitary tumor, and no tumor. Each image is stored as an individual .png file, and the dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application.
Dataset Structure
The dataset is organized into the following structure:
brain-cancer-4class/
train/
glioma_tumor/
image_001.png
image_002.png
...
meningioma_tumor/
image_001.png
image_002.png
...
pituitary_tumor/
image_001.png
image_002.png
...
no_tumor/
image_001.png
image_002.png
...
test/
glioma_tumor/
image_001.png
image_002.png
...
meningioma_tumor/
image_001.png
image_002.png
...
pituitary_tumor/
image_001.png
image_002.png
...
no_tumor/
image_001.png
image_002.png
...
Note: All file names must be unique across class folders. Only
.pngformat is supported by PrismRCL for image inputs.
Features
- Image Data: Each file contains a brain MRI scan in
.pngformat. - Classes: Four folders represent the classification targets:
glioma_tumormeningioma_tumorpituitary_tumorno_tumor
Usage (pre-split; optimal parameters)
Here is an example of how to load the dataset using PrismRCL:
C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 ^
data=C:\path\to\brain-cancer-4class\train testdata=C:\path\to\brain-cancer-4class\test ^
savemodel=C:\path\to\models\brain_cancer_4class.classify ^
log=C:\path\to\log_files stopwhendone
Explanation of Command:
- C:\PrismRCL\PrismRCL.exe: Path to the PrismRCL executable for classification
- chisquared: Specifies Chi-squared as the training evaluation method
- rclticks=10: Number of RCL iterations during training
- boxdown=0: RCL training parameter
- data=C:\path\to\brain-cancer-4class\train: Path to the training dataset
- testdata=C:\path\to\brain-cancer-4class\test: Path to the testing dataset
- savemodel=C:\path\to\models\brain_cancer_4class.classify: Output path for the trained model
- log=C:\path\to\log_files: Log file storage directory
- stopwhendone: Ends the session when training is complete
License
This dataset is licensed under the MIT License.
Original Source
This dataset was prepared from publicly available medical imaging archives and academic datasets. Please cite the appropriate dataset creators if you use this in research or applications. For example, similar datasets appear in:
Cheng, Jun, et al. "Enhanced performance of brain tumor classification via tumor region augmentation and partition." PloS One, 10.10 (2015): e0140381.
Additional Information
All files are preprocessed .png images to ensure compatibility with PrismRCL. No resizing or normalization is required when using PrismRCL version 2.4.0 or later.
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