siglip-base-patch16-224
by google
Sigmoid Loss for Language Image Pre-Training, efficient contrastive learning
google/siglip-base-patch16-224mixpeek://image_extractor@v1/google_siglip_base_v1Deploy siglip-base-patch16-224
Single-tenantMixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.
Overview
SigLIP replaces CLIP's softmax-based contrastive loss with a simple pairwise sigmoid loss, enabling more efficient training on larger batch sizes without requiring a global normalization step.
On Mixpeek, SigLIP offers a lighter-weight alternative to CLIP for visual embedding extraction, with comparable accuracy on many benchmarks while being faster to run at inference time.
Architecture
Vision Transformer (ViT-B/16) with 12 layers, 768-dim hidden size, 12 attention heads. Uses sigmoid contrastive loss instead of softmax, eliminating the need for large batch normalization.
How it runs
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so siglip-base-patch16-224 runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// The vector name has to match a vector index on the collection.
vectors: { "image-embedding": yourVector },
payload: { source_key: "archive/2026/asset-00412" },
},
],
}),
},
);
// Managed alternative, if this exact model is not the requirement:
// image_extractor@v1 runs google/siglip-base-patch16-224
// (768-d) over a bucket, with no inference of your own.Capabilities
- Efficient contrastive image-text learning
- 768-dimensional dense vector embeddings
- Lower memory footprint than CLIP ViT-L
- Strong zero-shot classification performance
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| ImageNet zero-shot | Top-1 Accuracy | 73.2% | Zhai et al., 2023: Table 1 |
| COCO (image→text) | Recall@1 | 62.7% | Zhai et al., 2023: Table 3 |
| ObjectNet | Top-1 Accuracy | 59.1% | Zhai et al., 2023: Table 2 |
Performance
Common Pipeline Companions
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Specification
Research Paper
Sigmoid Loss for Language Image Pre-Training
arxiv.orgBuild a pipeline with siglip-base-patch16-224
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free