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Datasets:
boboyes
/
fridge-segmentation

Tasks:
Image Segmentation
Object Detection
Modalities:
Image
Text
Languages:
English
Size:
n<1K
Tags:
fridge
device
storage
segmentation
YOLO
License:
Dataset card Data Studio Files
xet
Community
Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1901, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              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 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Refrigerator Segmentation Dataset (YOLO Seg)

Dataset Preview

This dataset contains annotated refrigerator images for instance segmentation training in YOLO format.

⚠️ Dataset Note

This repository contains the prepared segmentation dataset currently available in this project.

If you want to scale to a larger dataset, you can extend the pipeline with additional PDF/image extraction, labeling, and conversion scripts from this workspace.

Dataset Description

This dataset is designed for training refrigerator localization/segmentation models (YOLO segmentation workflow).
It includes real catalog-like and field-style images with varied angles, lighting, and clutter conditions.

Key Features

  • Task Type: Single-class refrigerator segmentation (fridges)
  • Annotation Format: YOLO segmentation labels (class_id x1 y1 x2 y2 ... xn yn)
  • Split Structure: train/, valid/, test/
  • Total Images: 523
  • Label Pairing: 1:1 image-label pairing verified for all splits
  • Normalized Coordinates: Polygon points normalized in [0, 1]

Split Summary

  • train: 401 images + 401 labels
  • valid: 77 images + 77 labels
  • test: 45 images + 45 labels

File Structure

  • train/images/, train/labels/
  • valid/images/, valid/labels/
  • test/images/, test/labels/
  • data.yaml: YOLO training configuration

Label Format

Each label row follows YOLO segmentation polygon format:

class_id x1 y1 x2 y2 ... xn yn
  • class_id: integer class index
  • Polygon must contain at least 3 points (6 coordinate values)
  • Coordinates are normalized by image width/height

Class Mapping

From data.yaml:

  • 0: fridges

Example Training Use (Ultralytics) 

yolo task=segment mode=train data=data.yaml model=yolo12s-seg.pt epochs=100 imgsz=640

Intended Use

  • Refrigerator ROI segmentation
  • Downstream cropping for type classification and attribute prediction
  • Benchmarking segmentation robustness on retail/catalog-style imagery

Limitations

  • Single-class setup (fridges) only
  • Dataset size may be limited for strong generalization in production
  • Domain shift may occur for unseen camera viewpoints, stores, or product styles

Citation

If you use this dataset, please cite this dataset repository and your project/workspace source.
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