sam-vit-huge
by facebook
Promptable foundation model for image segmentation
facebook/sam-vit-hugemixpeek://image_extractor@v1/facebook_sam_vit_huge_v1Overview
SAM (Segment Anything Model) is Meta's foundation model for image segmentation. Given prompts like points, boxes, or text, it produces high-quality object masks. Trained on SA-1B: the largest segmentation dataset with 1 billion masks on 11M images.
On Mixpeek, SAM powers pixel-level object segmentation for precise content understanding, enabling mask-based filtering and region-specific feature extraction.
Architecture
ViT-H image encoder (632M params) with a lightweight mask decoder. Produces 256x256 low-res masks refined to full resolution. Supports multiple prompt types: points, boxes, and masks.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so sam-vit-huge 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",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- Promptable segmentation with points, boxes, or masks
- Automatic mask generation for everything in an image
- Zero-shot transfer competitive with supervised models
- Trained on 1 billion masks (SA-1B dataset)
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| SA-1B (segmentation) | mIoU | 79.3 | Kirillov et al., 2023: Table 1 |
| COCO (instance seg.) | AP | 46.5 | Kirillov et al., 2023: Table 7 |
Performance
Image encoder runs once; mask decoder runs per prompt (~6ms)
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Specification
Research Paper
Segment Anything
arxiv.orgBuild a pipeline with sam-vit-huge
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