Weapondetection Grouped is a cleaned and consolidated version of the Subh775/WeaponDetection dataset. The author ccxhwmy merged 29 original classes into 3 distinct categories: GUN, KNIFE, and PERSON. The dataset was last updated on 2026-04-14.
Use Cases
- Train object detection models to identify firearms based on the GUN class.
- Develop models for detecting bladed weapons based on the KNIFE class.
- Build multi-class detection systems for security scenarios based on the combined GUN, KNIFE, and PERSON labels.
- Fine-tune pre-trained vision models on a cleaned dataset to reduce label ambiguity.
Strengths
- Original 29 ambiguous classes were consolidated into 3 distinct categories.
- The cleaning process aimed to remove label ambiguity and create stronger class definitions.
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 is unknown, which may limit suitability assessment.
Provenance
- Source
- Derived from the Subh775/WeaponDetection dataset on Hugging Face.
- Collection Method
- Cleaned, consolidated, and made training-ready by the author.
- Freshness
- Last updated 2026-04-14 01:14:02; freshness should be verified.