Datasets:

VDBBench
/

multimodal-embedding-1M

language:
  - en
license: apache-2.0
size_categories:
  - 1M<n<10M
task_categories:
  - feature-extraction
tags:
  - multimodal
  - embeddings
  - vector-search
  - benchmark
  - image-text
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: emb
      sequence: float32
  splits:
    - name: train
      num_examples: 1000000
    - name: test
      num_examples: 10000
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.parquet
      - split: test
        path: test.parquet

Multimodal Embedding 1M Benchmark Dataset

A vector search benchmark dataset containing 1M base vectors and 10K query vectors with pre-computed ground truth, generated from multimodal (image + text) inputs.

Dataset Description

Each embedding is produced by encoding an image-text pair into a single 4096-dimensional vector using Qwen3-VL-Embedding-8B, a state-of-the-art multimodal embedding model.

Source data: pixparse/cc3m-wds (Conceptual Captions 3M in WebDataset format)
Embedding model: Qwen3-VL-Embedding-8B via vLLM (runner="pooling", dtype=bfloat16)
Input format: Each sample consists of an image and its corresponding caption, embedded together as a multimodal input
Normalization: L2 normalized

Files

File	Rows	Columns	Description
`train.parquet`	1,000,000	`id` (int64), `emb` (list<float32>)	Base vectors from shards 0-199
`test.parquet`	10,000	`id` (int64), `emb` (list<float32>)	Query vectors from shards 200-201
`neighbors.parquet`	10,000	`id` (int64), `neighbors` (list<int64>)	Ground truth: top-100 nearest neighbors by Inner Product

Key Properties

Property	Value
Dimension	4096
Distance metric	Inner Product (IP)
Base size	1,000,000
Query size	10,000
Top-K (ground truth)	100
Vector dtype	float32
Base/Query overlap	None (disjoint shards)

Usage

from datasets import load_dataset

# Load base and query splits
ds = load_dataset("WenxingZhu/multimodal-embedding-1M")
train = ds["train"]  # 1M base vectors
test = ds["test"]    # 10K query vectors

# Load ground truth
import pyarrow.parquet as pq
gt = pq.read_table("neighbors.parquet").to_pandas()

# Access embeddings
import numpy as np
base_emb = np.array(train["emb"])      # (1000000, 4096)
query_emb = np.array(test["emb"])      # (10000, 4096)
neighbors = np.array(gt["neighbors"].tolist())  # (10000, 100)

Generation Details

Hardware: 4x NVIDIA A100-80GB (DGX)
Inference: vLLM v0.17.1, pooling mode, batch size 16
Throughput: ~25 samples/sec per GPU
Total time: ~2.75 hours for 1M embeddings
Prompt template: System message "Represent the user's input." + User message with image and text content

License

This dataset is released under the Apache 2.0 license. The source images and captions are from Conceptual Captions 3M.