Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: BadZipFile
Message: File is not a zip file
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 "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1029, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
).get_module()
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 637, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 291, in infer_module_for_data_files
split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 235, in infer_module_for_data_files_list
return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 262, in infer_module_for_data_files_list_in_archives
f.split("::")[0] for f in xglob(extracted, recursive=True, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1050, in xglob
fs, *_ = url_to_fs(urlpath, **storage_options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 395, in url_to_fs
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/registry.py", line 293, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 80, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/implementations/zip.py", line 62, in __init__
self.zip = zipfile.ZipFile(
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1354, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1419, in _RealGetContents
raise BadZipFile("File is not a zip file")
zipfile.BadZipFile: File is not a zip fileNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning: The task_ids "object-detection-yolo" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Chessboard Detection Dataset
This dataset consists of a total of 64,386 chessboard images and corresponding YOLO-format label files.
Dataset Breakdown
Images: 64,386 total
train: 57,928val: 6,458
Labels: 64,386 total (one
.txtper image)train: 57,928val: 6,458
Each label file contains bounding boxes for the pieces on the board using YOLO format. The dataset includes 12 classes:
- 6 white pieces
- 6 black pieces
Data Collection & Annotation
The dataset was generated using chess game data from the Lichess platform, which provides a massive monthly collection of games in PGN format. Each game includes a FEN string for every move, describing the position of all pieces on the board.
We used:
- The
python-chessAPI to convert FEN strings into rendered chessboard images. - A custom script to divide the board into 8×8 squares and extract object annotations from each FEN.
- These annotations were then converted into YOLO-format
.txtfiles for training object detection models.
Use Cases
This dataset is ideal for:
- Training object detection models (YOLOv5, YOLOv8, etc.)
- Detecting individual chess pieces on a board
- Converting board images back into digital game state (FEN)
License
This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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