---
license: cc-by-4.0
task_categories:
- image-text-to-text
- image-to-text
- text-classification
language:
- en
tags:
- Image
- Captioning
- Violence
- Detection
- Vision
- AI
- Data
- Sceince
- Deep
- Learning
pretty_name: 'Violence-Cap: The First Frame-Level Captioning Dataset for Violence Research'
size_categories:
- 10K<n<100K
---
# Violence-Cap Dataset

![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)

**Violence-Cap** is a video frame captioning dataset layered over the Real Life Violence Situations (RLVS) dataset. It is designed to provide granular, frame-level context for video violence analysis. 

Each record in the dataset pairs an individual video frame with:
1. A descriptive text caption.
2. An independent, frame-level binary violence label.

## Motivation & Key Findings
Video-level labels often fail to capture the temporal nuances of an event. In the creation of this dataset, 5,688 frames were extracted from videos globally labeled as *violent*. Upon human review, **1,243 frames (21.9%) were identified as *non-violent***. This highlights the critical necessity of per-frame annotation when training precise violence recognition and captioning models.

## Dataset Statistics & Splits
The dataset features pre-defined, video-level **train, validation, and test splits** to ensure no data leakage between frames of the same video during model evaluation.

* **Curated Set:** 10,907 frame–caption–label triples across 1,991 videos.
* **Full Set:** 10,952 frame–caption–label triples across all 2,000 videos.

## Files and Annotations
The annotations are distributed across two primary CSV files:

| File Name | Rows (Triples) | Video Count | Description |
| :--- | :--- | :--- | :--- |
| `curated_set.csv` | 10,907 | 1,991 | The thoroughly reviewed and cleaned dataset recommended for training and evaluation. |
| `full_set.csv` | 10,952 | 2,000 | The complete, unpruned dataset spanning all original RLVS videos. |

## License
This dataset is released under the **Creative Commons Attribution 4.0 International ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))** license.

## Related Work
Violence-Cap is built upon the RLVS dataset.

> Soliman, M. M., Hussein Kamal, M., Abd El-Massih Nashed, M., Mostafa, Y. M., Safwat Chawky, B., & Khattab, D. (2019). *Violence recognition from videos using deep learning techniques.* 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), 80–85.