README.md

---
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 1M<n<10M
---

# `PD12M`

This is a curated PD12M dataset for use with the [II-Commons](https://github.com/Intelligent-Internet/II-Commons) project.

## Dataset Details

### Dataset Description

This dataset comprises a curated [Public Domain 12M](https://source.plus/pd12m) image collection, refined by filtering for active image links. EXIF data was extracted, and images underwent preprocessing and feature extraction using [SigLIP 2](https://huggingface.co/papers/2502.14786). All vector embeddings are normalized 16-bit half-precision vectors optimized for L2 indexing with [vectorchord](https://github.com/tensorchord/vectorchord).

### Dataset Sources

This dataset is derived and organized from [Spawning/PD12M](http://huggingface.co/datasets/Spawning/PD12M). The original license information for the image can be found in the corresponding entry of the original database.

## Dataset Structure

- id: A unique identifier for the image.
- url: The URL of the image.
- caption: A caption for the image.
- caption_long: A long caption for the image.
- origin_width: The width of the original image in pixels.
- origin_height: The height of the original image in pixels.
- processed_width: The width of the processed image in pixels.
- processed_height: The height of the processed image in pixels.
- aspect_ratio: The aspect ratio of the image.
- exif: The EXIF data of the image.
- meta: The metadata of the image.
- created_at: The creation time of the image.
- updated_at: The update time of the image.
- source: The source organization of the image.
- vector: The vector embedding of the image.
- origin_source: The origin source of the image.
- license: The license of the image.

## Prerequisite

PostgreSQL 17 with extensions: [vectorchord](https://github.com/tensorchord/VectorChord) and [pg_search](https://github.com/paradedb/paradedb/tree/dev/pg_search)

The easiest way is to use our [Docker image](https://github.com/Intelligent-Internet/II-Commons/tree/main/examples/db), or build your own. Then load the [psql_basebackup](https://huggingface.co/datasets/Intelligent-Internet/pd12m/tree/psql_basebackup) branch, following the [Quick Start](https://github.com/Intelligent-Internet/II-Commons?tab=readme-ov-file#quick-start) 

Ensure extensions are enabled, connect to the database using the psql, and run the following SQL:

```sql
CREATE EXTENSION IF NOT EXISTS vchord CASCADE;
CREATE EXTENSION IF NOT EXISTS pg_search CASCADE;
```

## Uses

This dataset is available for a wide range of applications.

Here is a demo of how to use the dataset with [II-Commons](https://github.com/Intelligent-Internet/II-Commons).

### Create a Table in PostgreSQL

```sql
CREATE TABLE IF NOT EXISTS is_pd12m (
    id BIGSERIAL PRIMARY KEY,
    url VARCHAR NOT NULL,
    caption VARCHAR NOT NULL DEFAULT '',
    caption_long VARCHAR DEFAULT '',
    origin_width BIGINT NOT NULL DEFAULT 0,
    origin_height BIGINT NOT NULL DEFAULT 0,
    processed_width BIGINT NOT NULL DEFAULT 0,
    processed_height BIGINT NOT NULL DEFAULT 0,
    aspect_ratio DOUBLE PRECISION NOT NULL DEFAULT 0,
    exif JSONB NOT NULL DEFAULT '{}',
    meta JSONB NOT NULL DEFAULT '{}',
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    source JSONB NOT NULL DEFAULT '[]',
    vector halfvec(1152) DEFAULT NULL,
    origin_source VARCHAR DEFAULT '',
    license VARCHAR DEFAULT ''
);
```

### Load csv files to database

1. Load the dataset from local file system to a remote PostgreSQL server:

```sql
\copy is_pd12m FROM 'data/0000000.csv' CSV HEADER;
\copy is_pd12m FROM 'data/0000001.csv' CSV HEADER;
\copy is_pd12m FROM 'data/0000002.csv' CSV HEADER;
...
```

2. Load the dataset from the PostgreSQL server's file system:

```sql
copy is_pd12m FROM 'data/0000000.csv' CSV HEADER;
copy is_pd12m FROM 'data/0000001.csv' CSV HEADER;
copy is_pd12m FROM 'data/0000002.csv' CSV HEADER;
...
```

### Create Indexes

You need to create the following indexes for the best performance.

The `vector` column is a halfvec(1152) column, which is a 16-bit half-precision vector optimized for `L2` indexing with [vectorchord](https://github.com/tensorchord/vectorchord). You can get more information about the vector index from the [vectorchord](https://docs.vectorchord.ai/vectorchord/usage/indexing.html) documentation.

```sql
CREATE UNIQUE INDEX IF NOT EXISTS is_pd12m_url_index ON is_pd12m (url);
CREATE INDEX IF NOT EXISTS is_pd12m_origin_width_index ON is_pd12m (origin_width);
CREATE INDEX IF NOT EXISTS is_pd12m_origin_height_index ON is_pd12m (origin_height);
CREATE INDEX IF NOT EXISTS is_pd12m_processed_width_index ON is_pd12m (processed_width);
CREATE INDEX IF NOT EXISTS is_pd12m_processed_height_index ON is_pd12m (processed_height);
CREATE INDEX IF NOT EXISTS is_pd12m_aspect_ratio_index ON is_pd12m (aspect_ratio);
CREATE INDEX IF NOT EXISTS is_pd12m_exif_index ON is_pd12m USING gin(exif);
CREATE INDEX IF NOT EXISTS is_pd12m_meta_index ON is_pd12m USING gin(meta);
CREATE INDEX IF NOT EXISTS is_pd12m_source_index ON is_pd12m USING gin(source);
CREATE INDEX IF NOT EXISTS is_pd12m_created_at_index ON is_pd12m (created_at);
CREATE INDEX IF NOT EXISTS is_pd12m_updated_at_index ON is_pd12m (updated_at);
CREATE INDEX IF NOT EXISTS is_pd12m_vector_index ON is_pd12m USING vchordrq (vector halfvec_l2_ops)  WITH (options = $$
    residual_quantization = true
    [build.internal]
    lists = [20000]
    build_threads = 6
    spherical_centroids = false
$$);
CREATE INDEX IF NOT EXISTS is_pd12m_caption_index ON is_pd12m (caption) WHERE caption = '';
CREATE INDEX IF NOT EXISTS is_pd12m_caption_long_index ON is_pd12m (caption_long) WHERE caption_long = '';
CREATE INDEX IF NOT EXISTS is_pd12m_vector_null_index ON is_pd12m (vector) WHERE vector IS NULL;
```

### Query with II-Commons

Click this link to learn how to query the dataset with [II-Commons](https://github.com/Intelligent-Internet/II-Commons).