| | --- |
| | 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`](./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 |
| | |
| | ```python |
| | 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()) |
