download_findingemo_data: Download FindingEmo-Light Dataset

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Download FindingEmo-Light Dataset

Description

Downloads the FindingEmo-Light dataset using the official PyPI package. This dataset contains 25k images with emotion annotations including valence, arousal, and discrete emotion labels, focusing on complex naturalistic scenes with multiple people in social settings.

Usage

download_findingemo_data(
  target_dir,
  max_images = NULL,
  randomize = FALSE,
  skip_existing = TRUE,
  force = FALSE
)

Arguments

target_dir	Character. Directory to download the dataset to.
max_images	Integer. Maximum number of images to download (optional).
randomize	Logical. If TRUE and max_images is specified, randomly select images for download. Useful for creating test/benchmark subsets (default: FALSE).
skip_existing	Logical. Whether to skip download if dataset already exists (default: TRUE).
force	Logical. Force download even if dataset exists (default: FALSE).

Details

This function requires the findingemo-light Python package to be installed. Use setup_modules() to install required dependencies before calling this function.

The FindingEmo dataset is described in: Mertens, L. et al. (2024). "FindingEmo: An Image Dataset for Emotion Recognition in the Wild". NeurIPS 2024 Datasets and Benchmarks Track.

The dataset uses a flat directory structure with all images stored directly in the images/ subdirectory, annotations.csv and urls.json at the root level.

**Note**: For copyright reasons, the dataset provides URLs and annotations only. Images are downloaded on-demand from their original sources.

Value

A list containing:

• success: Logical indicating if download was successful

• message: Character string with status message

• target_dir: Path to downloaded data

• annotation_file: Path to annotation file (if successful)

• urls_file: Path to URLs file (if successful)

• image_count: Number of images downloaded (if any)

• annotations: Full annotations data.frame (raw)

• evaluation_data: Data.frame filtered to downloaded images with columns suitable for evaluation workflows (id, truth, image_file, image_path, valence, arousal, emo8_label, emotion)

• evaluation_csv: Path to saved CSV of evaluation_data

• matched_count: Number of annotations matched to downloaded images

See Also

load_findingemo_annotations, setup_modules

Examples

## Not run: 
# First install required modules
setup_modules()

# Download dataset to local directory
result <- download_findingemo_data("./findingemo_data")

if (result$success) {
  cat("Dataset downloaded to:", result$target_dir)
  cat("Images downloaded:", result$image_count)
}

# Download random subset for testing/benchmarking
result <- download_findingemo_data(
  target_dir = "./findingemo_test",
  max_images = 100,
  randomize = TRUE
)

# Download subset with flat directory structure (always used)
result <- download_findingemo_data(
  target_dir = "./findingemo_subset",
  max_images = 50
)

# Force re-download
result <- download_findingemo_data(
  target_dir = "./findingemo_data",
  force = TRUE
)

## End(Not run)

transforEmotion documentation built on Jan. 8, 2026, 5:06 p.m.