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    Models/Segmentation/facebook/sam-vit-huge
    HFSegmentationApache 2.0

    sam-vit-huge

    by facebook

    Promptable foundation model for image segmentation

    Identifiers
    Model ID
    facebook/sam-vit-huge
    Feature URI
    mixpeek://image_extractor@v1/facebook_sam_vit_huge_v1

    Overview

    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

    Pixel-level content segmentation in video and images
    Automated mask generation for training data creation
    Region-specific feature extraction pipelines
    Interactive annotation assistance

    Benchmarks

    DatasetMetricScoreSource
    SA-1B (segmentation)mIoU79.3Kirillov et al., 2023: Table 1
    COCO (instance seg.)AP46.5Kirillov et al., 2023: Table 7

    Performance

    Input Size1024×1024 px
    GPU Latency~42ms / image (A100)
    CPU Latency~620ms / image
    GPU Throughput~24 images/sec (A100)
    GPU Memory~2.6 GB

    Image encoder runs once; mask decoder runs per prompt (~6ms)

    Specification

    FrameworkHF
    Organizationfacebook
    FeatureSegmentation
    Outputmask + label
    Modalitiesvideo, image
    RetrieverMask Filter
    Parameters632M
    LicenseApache 2.0
    Downloads/mo3.2M

    Research Paper

    Segment Anything

    arxiv.org

    Build 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