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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
batch = f.read(self.config.chunksize)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
out = read(*args, **kwargs)
File "<frozen codecs>", line 325, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x93 in position 0: invalid start byte
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 1694, 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 1880, 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.
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**Foxing\_feces** contains: |
* **dataset\_combo\_alpha** - combination of synthetic (generated with Alpha blending) and real data (Real test data). |
* **dataset\_combo\_cmyk** - combination of synthetic (generated with Cmyk blending) and real data (Real test data). |
* **syn\_dataset\_alpha -** only synthetic data generated with alpha blending (Real test data). |
* **syn\_dataset\_cmyk -** only synthetic data generated with cmyk blending (Real test data). |
* **real\_dataset -** only real data. |
**Fungi\_bacteria** contains: |
* **syn\_alpha_blend -** 12 classes (6- bacteria, 6-fungus) - all synthetic data generated with alpha blend method. |
* **syn\_cmyk_blend -** 12 classes (6- bacteria, 6-fungus) - all synthetic data generated with cmyk blend method. |
* **syn\_cmyk\_texture -** 12 classes (6- bacteria, 6-fungus) - all synthetic data generated with cmyk texture method. |
* **syn\_cmyk\_texture\_feather_noise -** 12 classes (6- bacteria, 6-fungus) - all synthetic data generated with cmyk texture feather noise method. |
* **syn\_copy\_paste -** 12 classes (6- bacteria, 6-fungus) - all synthetic data generated with copy paste method. |
# foxing_project_try > 2025-04-04 7:19pm |
https://universe.roboflow.com/bakalarka-ofbvw/foxing_project_try |
Provided by a Roboflow user |
License: CC BY 4.0 |
**dataset_combo** |
**Version:** 2025-04-04 |
**Exported via:** [Roboflow](https://roboflow.com) |
**Annotation format:** YOLOv8 |
**Total images:** 1,512 |
## Dataset Description |
This dataset contains annotations of stains found on historical documents for the purpose of object detection model training. It includes five stain categories: **foxing and feces**. |
The dataset was created as part of the **HIPSI research project** (APVV-23-0250), in collaboration with the **Institute of Molecular Biology of the Slovak Academy of Sciences** and the **Academy of Fine Arts and Design in Bratislava (VŠVU)**. |
Images were partially annotated and preprocessed using **Roboflow**, and exported in **YOLOv8 format**. |
## Image Preprocessing |
Each image was processed as follows: |
- Auto-orientation correction (EXIF orientation removed) |
- Resized to **1280x1280** (stretched) |
## Augmentation (3 variants per image) |
The following augmentations were applied: |
- **50% probability of horizontal flip** |
- **Random 90° rotation**: none / clockwise / counterclockwise / upside-down (equal chance) |
- **Random crop**: 0–20% of the image |
- **Random rotation**: between -15° and +15° |
- **Random brightness adjustment**: ±15% |
- **Random exposure adjustment**: ±10% |
## License |
This dataset is intended for **research and non-commercial use only**. |
foxing_project_try - v29 2025-04-04 7:19pm |
============================== |
This dataset was exported via roboflow.com on April 4, 2025 at 5:23 PM GMT |
Roboflow is an end-to-end computer vision platform that helps you |
* collaborate with your team on computer vision projects |
* collect & organize images |
* understand and search unstructured image data |
* annotate, and create datasets |
* export, train, and deploy computer vision models |
* use active learning to improve your dataset over time |
For state of the art Computer Vision training notebooks you can use with this dataset, |
visit https://github.com/roboflow/notebooks |
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com |
The dataset includes 1512 images. |
Stains-KqL0 are annotated in YOLOv8 format. |
The following pre-processing was applied to each image: |
* Auto-orientation of pixel data (with EXIF-orientation stripping) |
* Resize to 1280x1280 (Stretch) |
The following augmentation was applied to create 3 versions of each source image: |
* 50% probability of horizontal flip |
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down |
* Randomly crop between 0 and 20 percent of the image |
* Random rotation of between -15 and +15 degrees |
* Random brigthness adjustment of between -15 and +15 percent |
* Random exposure adjustment of between -10 and +10 percent |
train: ../train/images |
val: ../valid/images |
test: ../test/images |
nc: 2 |
names: ['0', '5'] |
End of preview.