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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    FileNotFoundError
Message:      [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f1834a9ae70>'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
                  with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
                       ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 967, in xopen
                  return open(main_hop, mode, *args, **kwargs)
              FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f1834a9ae70>'

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Dataset Description

The Retail Object Detector dataset supports training and evaluating computer vision models for general object detection in retail environments. It contains 749 labeled .PNG images across train, dev, and test splits, combining real retail images with AI generated synthetic images to improve model robustness.

Each image includes a corresponding Pascal VOC XML annotation with bounding boxes and a single class label 'obj', intended for research, model development, and inventory monitoring use cases, performace may vary with lighting, occlusion, camera angle, and image quality.

Intended Use

This dataset is intended for research, experimentation, educational applications, and early-stage development of retail computer vision systems. It can be used for developing and evaluating object detection models focused on identifying and localizing items in shelf-like environments, with potential applications including shelf item detection, stock visibility analysis, and planogram compliance experiments.

Dataset Strengths

The dataset combines synthetic and non-synthetic imagery, providing two different sources of visual data for experimentation. Images represent multiple shelf and storage configurations, while maintaining a consistent object-detection annotation structure. The dataset uses a single OBJ class, allowing it to be used as a general-purpose object localization dataset without requiring product-specific classification labels.

Dataset formatting

ROD_img1.0.png ROD_img2.0.png

Image formatting

ROD_img3.0.png

Citation

@dataset{ratio1_drone_dataset_2026,
  author       = {Ratio1},
  title        = {Retail Object Detection},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ratio1/Retail_Object_Detection},
  version      = {v1.0}
}
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