---
license: mit
dataset_info:
  features:
  - name: id
    dtype: string
  - name: instructions
    dtype: string
  - name: answer
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 243404574.5
    num_examples: 8956
  - name: validation
    num_bytes: 25165334.5
    num_examples: 1116
  download_size: 196488037
  dataset_size: 268569909
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
task_categories:
- question-answering
- summarization
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---


# Emotion Dataset

## Dataset Description

### Dataset Summary

BoEM: A Multimodal VQA Dataset for Fine-Grained Facial Emotion Recognition.


This dataset contains facial expressions with corresponding emotion labels. Each image is labeled with emotions such as fear, happiness, sadness, etc., along with intensity information.

### Languages

The dataset annotations are in English.

## Dataset Structure

### Data Instances

Each instance in the dataset consists of:
- `id`: Unique identifier for the image
- `image`: The facial expression image
- `instructions`: The prompt/instruction given to annotate the image
- `answer`: The emotion label assigned to the image

### Data Fields

- `id`: String - Unique identifier for each sample
- `image`: Image - The facial expression image
- `instructions`: String - The prompt/instruction given to annotate the image
- `answer`: String - The emotion label/description

### Data Splits

The dataset is divided into training and validation splits.

## Dataset Creation

### Source Data

The dataset contains facial expressions collected for emotion recognition tasks.

### Annotations

The images are annotated with emotion labels and intensity information.

## Considerations for Using the Data

### Licensing Information

MIT

@misc{emotionbench2026submission,
  title        = {BoEM: A Multimodal VQA Dataset for Emotion Recognition},
  author       = {Hoang Nam Dang, Lockheed Martin, Mei-Huei Tang},
  year         = {2026},
  note         = {Manuscript submitted to CVPR 2026},
}