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    Models/Segmentation/facebook/sam2.1-hiera-large
    HFSegmentationapache-2.0

    sam2.1-hiera-large

    by facebook

    Unified promptable segmentation for images and video with streaming memory

    79Kdl/month
    144likes
    224Mparams
    Identifiers
    Model ID
    facebook/sam2.1-hiera-large
    Feature URI
    mixpeek://image_extractor@v1/facebook_sam2_large_v1

    Overview

    SAM 2 extends SAM to video with a streaming memory architecture for real-time processing. It's 6x faster than SAM on images with better accuracy, and the first foundation model that segments and tracks objects across video frames with prompts.

    On Mixpeek, SAM 2 enables video-native segmentation: track objects across frames, segment specific items at any point in a video, and extract per-object features over time.

    Architecture

    Hiera image encoder with streaming memory for temporal context. SAM 2.1 Large: 224.4M params, 39.5 FPS on A100. Memory attention modules propagate masks across frames without re-computing the full image encoder.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so sam2.1-hiera-large 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

    • Video object segmentation and tracking
    • 6x faster than SAM on images
    • Streaming memory architecture for real-time video
    • Multi-object tracking with mask propagation
    • Image segmentation with improved accuracy

    Use Cases on Mixpeek

    Video object tracking and segmentation across frames
    Real-time content understanding in video streams
    Per-object feature extraction in video pipelines
    Interactive video annotation and editing

    Benchmarks

    DatasetMetricScoreSource
    SA-V (video seg.)J&F79.5Ravi et al., 2024: Table 1
    DAVIS 2017 (val)J&F82.0Ravi et al., 2024: Table 2

    Performance

    Input Size1024×1024 px
    GPU Latency~18ms / frame (A100)
    GPU Throughput~55 frames/sec (A100)
    GPU Memory~2.8 GB

    Streaming architecture: processes video frames sequentially with memory

    Specification

    FrameworkHF
    Organizationfacebook
    FeatureSegmentation
    Outputmask + label
    Modalitiesvideo, image
    RetrieverMask Filter
    Parameters224M
    Licenseapache-2.0
    Downloads/mo79K
    Likes144

    Research Paper

    SAM 2: Segment Anything in Images and Videos

    arxiv.org

    Build a pipeline with sam2.1-hiera-large

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