metadata
license: cc-by-nc-4.0
language:
- en
size_categories:
- 10K<n<100K
📚 HistVis Dataset
HistVis is a dataset designed to evaluate how text-to-image models represent cultural and historical variations in human activities. It contains images generated by multiple models across temporal prompts and activity categories.
📋 Dataset Structure
The main metadata is stored in dataset.csv, with one row per image. Below is a description of each column:
| Column | Description |
|---|---|
image_path |
Relative path to the image file within the repository. These correspond to generations by different models under specific historical prompts. |
model |
The name of the text-to-image model used to generate the image (e.g., Flux_Schnell, SD_3, SD_XL). |
historical_period |
The historical era or century the prompt refers to (e.g., 19th_century, 1920s). This is the temporal condition imposed in the prompt. |
universal_human_activity |
The prompt used to describe the universal human activity, such as "a person listening to music" or "a person laughing with a friend". |
category |
The broader conceptual category of the human activity (e.g., Health and Well-being, Art, "Music"). This groups related prompts under common cultural dimensions. |
🧾 Prompt Format
Each image in the dataset was generated using the following prompt template:
"a [universal_human_activity] in the [historical_period]"
For example:
- "a person listening to music in the 1950s"
- "a person laughing with a friend in the 19th century"
💻 Using the Dataset
You can access the HistVis dataset using the Hugging Face Datasets library. Below are examples showing how to load and explore the dataset.
Basic Usage
from datasets import load_dataset
import pandas as pd
# Load the dataset metadata (CSV only)
dataset = load_dataset('csv', data_files='https://huggingface.co/datasets/latentcanon/HistVis/resolve/main/dataset.csv')
# Convert to pandas DataFrame for easier manipulation
df = pd.DataFrame(dataset['train'])
print(f"Dataset contains {len(df)} entries")
# View first few entries
print(df.head())