jrmiller/coco-2017-siglip2-embeddings

COCO 2017 SigLIP 2 Image Embeddings

Pre-computed image embeddings for the COCO 2017 dataset, generated with Google's SigLIP 2 (SoViT-400M, 384px).

Overview

Property	Value
Model	google/siglip2-so400m-patch14-384
Vector dimensions	1152
Normalization	L2-normalized (unit vectors)
Source dataset	COCO 2017
Image resolution	384 x 384 (resized by SigLIP 2 processor)

Dataset Structure

Schema

Each row contains the embedding, metadata, and the raw image bytes for a single COCO image:

Column	Type	Description
image_id	int64	COCO image ID
file_name	string	Original filename (e.g. 000000000009.jpg)
caption	string	First COCO caption (empty for test/unlabeled splits)
coco_url	string	Original COCO download URL
width	int64	Original image width in pixels
height	int64	Original image height in pixels
split	string	Dataset split (train, val, test, or unlabeled)
vector	float32[1152]	L2-normalized SigLIP 2 image embedding
image_bytes	binary	Raw JPEG image bytes

LanceDB table

The lancedb/ directory contains the same data in Lance format, ready to load directly with LanceDB:

import lancedb

db = lancedb.connect("lancedb")
table = db.open_table("coco_clip_embeddings")

# Vector search
results = table.search(query_vector).limit(10).to_pandas()

# Images come back inline — no external storage needed
from PIL import Image
import io
img = Image.open(io.BytesIO(results.iloc[0]["image_bytes"]))

Usage

Load into LanceDB for vector search

import lancedb

db = lancedb.connect("lancedb")
table = db.open_table("coco_clip_embeddings")

# Find similar images
query_vec = df.iloc[0]["vector"]
results = table.search(query_vec).limit(5).to_pandas()

Compute similarity between images

import numpy as np

vec_a = np.array(df.iloc[0]["vector"])
vec_b = np.array(df.iloc[1]["vector"])
cosine_sim = np.dot(vec_a, vec_b)  # vectors are already L2-normalized

Generation

Embeddings were generated using the opensearch-lancedb-migration project:

# Download COCO images
uv run python -m src.cli download --split val

# Generate embeddings
uv run python -m src.cli embed

# Upload to Hugging Face
uv run python -m src.cli upload username/coco-2017-siglip2-embeddings --upload lancedb

License

The embeddings and code are released under the MIT License. The underlying COCO images are subject to the COCO Terms of Use.