---
pretty_name: BagBuddy Dataset
size_categories:
- n<1K
task_categories:
- image-classification
- object-detection
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: caption
    dtype: string
  - name: bucket
    dtype: string
  - name: sample_id
    dtype: string
  - name: labels
    list: string
  - name: annotation_indices
    list: int64
  - name: annotation_x
    list: int64
  - name: annotation_y
    list: int64
  - name: annotation_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 12169999
    num_examples: 40
  download_size: 12169290
  dataset_size: 12169999
---

# LLM-Pack: Grocery Detection Dataset

A small object detection and scene understanding dataset containing tabletop grocery scenes with annotated item names and object locations.
The dataset consists of 40 images with varying object counts, designed for evaluating object detection, counting, and multimodal reasoning systems in cluttered grocery scenarios.

## Dataset Overview

- **Total scenes:** 40
- **Object counts per scene:** 6, 8, 10, 12, 14, 16, 18, or 20 items
- **Samples per object-count category:** 5
- **Annotations:** Object names + object center coordinates
- **Image resolution:** 1920×1080
- **Task type:** Object detection / scene understanding / counting

## Dataset Structure

Each sample contains:

```python
{
    "image": PIL.Image,
    "caption": str,
    "bucket": str, # number of items on the image
    "sample_id": str,

    "labels": List[str],
    "annotation_indices": List[int],
    "annotation_x": List[int],
    "annotation_y": List[int],

    "annotation_count": int
}
```

## Annotation Format

Object annotations are stored as aligned lists.

Example:

```python
{
    "labels": [
        "Glass Beer Bottle",
        "Apples",
        "Noodles in Plastic Bag"
    ],

    "annotation_x": [1480, 1251, 1123],
    "annotation_y": [445, 822, 810]
}
```

Each `(annotation_x[i], annotation_y[i])` pair corresponds to the center position of `labels[i]` in the image.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset(
    "Yannik019/llm_pack_detection",
    split="train"
)

print(dataset)
```

## Example

A full usage example is available here:

- [example.py](https://huggingface.co/datasets/Yannik019/llm_pack_detection/blob/main/example.py)

## Intended Use

This dataset is intended for:

- Object detection benchmarking
- Vision-language model evaluation
- Scene understanding research
- Tabletop grocery perception
- Referring object localization

## Citation

```bibtex
@misc{blei2025llmpackintuitivegroceryhandling,
      title={LLM-Pack: Intuitive Grocery Handling for Logistics Applications}, 
      author={Yannik Blei and Michael Krawez and Tobias Jülg and Pierre Krack and Florian Walter and Wolfram Burgard},
      year={2025},
      eprint={2503.08445},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2503.08445}, 
}
```