---
task_categories:
- object-detection
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    dtype: string
  - name: annotated_image
    dtype: image
  splits:
  - name: train
    num_bytes: 33193824946
    num_examples: 52638
  download_size: 32556262135
  dataset_size: 33193824946
license: apache-2.0
language:
- en
tags:
- computer-vision
- object-detection
- computer-vision
- vision
- image
- bounding-boxes
- multimodal
- detection
- machine-learning
- deep-learning
- coco-format
- open-vocabulary-detection
- auto-annotation
- vlm
size_categories:
- 10K<n<100K
---


## **OpenDetection-50K-Remastered**

**OpenDetection-50K-Remastered** is an object detection dataset built primarily from general, publicly available images, which make up the major part of the input imagery, along with other publicly available datasets including the COCO dataset and Blip3o. General, all-class object detection annotations were produced using a custom-built RF-DETR computer vision pipeline driven by an object-manager task, yielding per-image object labels, confidence scores, and bounding boxes at scale.

### Dataset Details

- Images: 52,638, primarily general publicly available images forming the major part of the dataset, along with other publicly available datasets including the COCO dataset and Blip3o
- Annotations: object labels, label IDs, confidence scores, and bounding boxes, produced via a custom RF-DETR detection pipeline
- Format: parquet, optimized-parquet
- Split: train (52,638 rows)
- Total size: 32.6 GB
- Language: English
- License: Apache 2.0

### Source Data

The major part of the input imagery consists of general, publicly available images. This is supplemented by other publicly available datasets, including the COCO dataset and Blip3o, to build a broad and diverse pool of images for detection.

### Annotation Pipeline

Objects were detected using a custom-built pipeline based on RF-DETR. An object-manager task orchestrates detection across all images to produce general, all-object annotations for each image, covering multiple object classes per image with associated confidence scores and bounding box coordinates.

### Compute Infrastructure

Dataset generation was run using [Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs), which provided the compute infrastructure supporting large-scale annotation across the full image set.

## Dataset Structure

The dataset contains a single train split with the following fields.

| Field | Type | Description |
|---|---|---|
| image | image | Source input image |
| objects | string | JSON-encoded list of detections, each with `label`, `label_id`, `score`, and `bbox` |
| annotated_image | image | Source image rendered with bounding box annotations overlaid |

## Uses

This dataset can be used for training or fine-tuning object detection models, benchmarking detection pipelines, and building open-vocabulary or auto-annotation workflows that require labeled bounding boxes across diverse image sources.

## Licensing

This dataset is released under the Apache 2.0 license. Users should also refer to the terms of use for the underlying source imagery and datasets.

## Acknowledgements

- [COCO dataset](https://cocodataset.org/#download)
- Blip3o
- [RF-DETR](https://rfdetr.roboflow.com/latest/)
- [Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs) for compute infrastructure supporting dataset generation, thanks for the compute support

## Citation

```bibtex
@misc{prithiv_sakthi_2026,
	author       = { Prithiv Sakthi },
	title        = { OpenDetection-50K-Remastered (Revision ea85cab) },
	year         = 2026,
	url          = { https://huggingface.co/datasets/prithivMLmods/OpenDetection-50K-Remastered },
	doi          = { 10.57967/hf/9516 },
	publisher    = { Hugging Face }
}
```