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    HFDepth EstimationApple Sample Code License

    DepthPro

    by apple

    Zero-shot metric monocular depth estimation with sharp boundaries in under a second

    Identifiers
    Model ID
    apple/DepthPro
    Feature URI
    mixpeek://image_extractor@v1/apple_depthpro_v1

    Overview

    DepthPro is Apple's foundation model for zero-shot metric monocular depth estimation, producing 2.25-megapixel depth maps (1536x1536) in 0.3 seconds on a V100 GPU. Unlike relative depth models, DepthPro predicts absolute metric depth without requiring camera intrinsics, and includes a built-in focal length estimator. Its multi-scale ViT architecture with a shared DINOv2 encoder and DPT-like fusion stage preserves sharp object boundaries.

    On Mixpeek, DepthPro enables metric-accurate spatial understanding of images and video frames, powering use cases like 3D scene reconstruction, spatial filtering in retrieval, and depth-aware content organization.

    Architecture

    Multi-scale Vision Transformer with shared DINOv2 encoder processing image patches at multiple resolutions. DPT-like fusion stage merges and upsamples features for dense prediction. Built-in focal length estimation head. Outputs 1536x1536 metric depth maps with absolute scale.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so DepthPro 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

    • Zero-shot metric depth (absolute scale, no camera intrinsics needed)
    • 2.25-megapixel output (1536x1536) in 0.3s
    • Sharp boundary preservation via multi-scale architecture
    • Built-in focal length estimation from a single image
    • State-of-the-art boundary accuracy metrics

    Use Cases on Mixpeek

    3D scene reconstruction from single images or video frames
    Depth-aware retrieval and spatial filtering in media pipelines
    Augmented reality content creation with metric-accurate depth

    Benchmarks

    DatasetMetricScoreSource
    NYUv2AbsRel0.036Bochkovskii et al., 2024: Depth Pro paper
    KITTIAbsRel0.039Bochkovskii et al., 2024: Depth Pro paper
    Boundary F1F1 (depth edges)State-of-the-artBochkovskii et al., 2024: Depth Pro paper

    Performance

    Input SizeVariable (multi-scale, outputs 1536x1536)
    GPU Latency~300ms / image (V100)
    CPU Latency~2.5s / image
    GPU Throughput~12 images/sec (A100)
    GPU Memory~2.5 GB

    Specification

    FrameworkHF
    Organizationapple
    FeatureDepth Estimation
    Outputdepth map
    Modalitiesvideo, image
    RetrieverDepth Filter
    Parameters~350M
    LicenseApple Sample Code License
    Downloads/mo520K

    Research Paper

    Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

    arxiv.org

    Build a pipeline with DepthPro

    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