README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    dtype: string
  - name: annotated_image
    dtype: image
  splits:
  - name: train
    num_bytes: 11671106327
    num_examples: 30000
  download_size: 13858153622
  dataset_size: 11671106327
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- unified
- computer-vision
- object-detection
- vision
- image
- bounding-boxes
- multimodal
- detection
- machine-learning
- deep-learning
- coco-format
- open-vocabulary-detection
- auto-annotation
- vlm
license: apache-2.0
task_categories:
- object-detection
language:
- en
pretty_name: OpenDetection
size_categories:
- 10K<n<100K
---

> [!IMPORTANT]
> fully cleaned and unified version: [prithivMLmods/OpenDetection-80K-Unified-Cleaned](https://huggingface.co/datasets/prithivMLmods/OpenDetection-80K-Unified-Cleaned)

## **OpenDetection-30K-Human-Preferences**

**OpenDetection-30K-Human-Preferences** is an object detection dataset built primarily from general human-preference, publicly available images, which make up the majority of the input imagery, together with additional publicly available datasets. The dataset contains object detection annotations generated using automated computer vision pipelines and is intended for research, benchmarking, and training object detection models. Each sample includes the original image, structured object annotations, and a rendered visualization showing the detected objects. This is the **uncleaned** version of the dataset and may contain samples with empty or null object annotations. It is provided to support research scenarios where the original annotation outputs are required without additional filtering or post-processing.

## Dataset Statistics

| Property | Value |
|-----------|-------|
| Number of Samples | 30,000 |
| Image Format | RGB |
| Annotation Format | JSON |
| Visualization | Annotated Image |
| Dataset Format | Optimized Parquet |

## Important Note

This dataset is the **original uncleaned version**.

Some samples may contain:

- Empty object annotations (`objects == []`)
- Null or missing detection results
- Images without detected objects

These samples have been intentionally retained to preserve the original outputs generated by the annotation pipeline.

## Dataset Structure

Each sample contains the following fields:

| Column | Type | Description |
|---------|------|-------------|
| `image` | Image | Original input image |
| `objects` | List | Object detection annotations containing labels, label IDs, confidence scores, and bounding boxes |
| `annotated_image` | Image | Visualization of the image with rendered bounding boxes |

Example:

```python
sample = ds[0]

print(sample.keys())

# dict_keys([
#     "image",
#     "objects",
#     "annotated_image"
# ])
```

## Loading the Dataset

```python
from datasets import load_dataset

ds = load_dataset(
    "prithivMLmods/OpenDetection-30K-Human-Preferences",
    split="train"
)
```

## Example Usage

```python
from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset(
    "prithivMLmods/OpenDetection-30K-Human-Preferences",
    split="train"
)

sample = ds[0]

image = sample["image"]
objects = sample["objects"]
annotated = sample["annotated_image"]

print("Detected Objects:")
print(objects)

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

axes[0].imshow(image)
axes[0].set_title("Image")
axes[0].axis("off")

axes[1].imshow(annotated)
axes[1].set_title("Annotated Image")
axes[1].axis("off")

plt.show()
```

## Dataset Features

- 30K human-preference image samples
- Object detection annotations generated using automated pipelines
- Original unfiltered annotation outputs
- May contain empty or null object annotations
- Bounding box visualizations for every sample
- Optimized Parquet format for efficient loading
- Compatible with the Hugging Face Datasets library
- Suitable for object detection research, benchmarking, annotation analysis, and multimodal computer vision

## License

This dataset is released under the **Apache-2.0 License**.