OpenDetection-30K-Human-Preferences: Object Detection Annotations from Public Images

OpenDetection-30K-Human-Preferences is an object detection dataset built primarily from general human-preference, publicly available images. The dataset contains object detection annotations generated using automated computer vision pipelines. It was created by prithivMLmods and was last updated on Hugging Face on 2026-07-11.

Use Cases

• Train object detection models based on annotations from publicly available images.
• Fine-tune vision models using data generated from automated annotation pipelines.
• Study the characteristics of object detection datasets built from human-preference imagery.
• Benchmark model performance on a dataset sourced from multiple public image collections.

Strengths

• Annotations were generated using automated computer vision pipelines.
• The dataset is built from publicly available images, suggesting broad accessibility of source material.
• A fully cleaned and unified version (OpenDetection-80K) is referenced, indicating ongoing curation.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.

Provenance

Source

Hugging Face dataset by author prithivMLmods.

Collection Method

Built from general human-preference, publicly available images and additional publicly available datasets, with annotations generated using automated computer vision pipelines.

Freshness

Last updated 2026-07-11 15:21:40; freshness should be verified.