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    Models/Spatial Understanding/depth-anything/Depth-Anything-V2-Large
    HFDepth Estimationcc-by-nc-4.0

    Depth-Anything-V2-Large

    by depth-anything

    Foundation model for monocular depth estimation with synthetic-to-real training

    75Kdl/month
    164likes
    335Mparams
    Identifiers
    Model ID
    depth-anything/Depth-Anything-V2-Large
    Feature URI
    mixpeek://image_extractor@v1/depth_anything_v2_large_v1

    Overview

    Depth Anything V2 Large is a 335M-parameter monocular depth estimation model that produces dense per-pixel depth maps from single images. Built on a DINOv2-Large encoder with a DPT decoder, it is trained via a teacher-student paradigm: a giant ViT-G teacher learns from 595K synthetic images, then supervises student models on 62M pseudo-labeled real images to bridge the synthetic-to-real domain gap.

    On Mixpeek, Depth Anything V2 extracts depth maps from video frames and images, enabling spatial-aware retrieval such as finding scenes with specific depth compositions, foreground/background separation, or 3D layout understanding.

    Architecture

    DINOv2-Large (ViT-L) encoder with 24 layers feeding into a DPT (Dense Prediction Transformer) decoder. Intermediate features from DINOv2 are fused at multiple scales for dense depth prediction. Teacher-student training with ViT-G teacher on synthetic data.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so Depth-Anything-V2-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

    • Dense per-pixel relative depth estimation
    • 10x faster than diffusion-based depth models
    • Robust across indoor, outdoor, and synthetic scenes
    • Fine-grained boundary preservation
    • Metric depth variant available for absolute scale

    Use Cases on Mixpeek

    Spatial-aware video retrieval (find scenes by depth composition or layout)
    3D scene understanding for augmented reality content pipelines
    Foreground/background separation in visual effects and media production

    Benchmarks

    DatasetMetricScoreSource
    NYUv2AbsRel0.043Yang et al., 2024: Depth Anything V2 paper
    KITTIAbsRel0.044Yang et al., 2024: Depth Anything V2 paper
    SintelAbsRel0.280Yang et al., 2024: Depth Anything V2 paper

    Performance

    Input Size518x518 px (default)
    GPU Latency~12ms / image (A100)
    CPU Latency~180ms / image
    GPU Throughput~83 images/sec (A100)
    GPU Memory~1.4 GB

    Specification

    FrameworkHF
    Organizationdepth-anything
    FeatureDepth Estimation
    Outputdepth map
    Modalitiesvideo, image
    RetrieverDepth Filter
    Parameters335M
    Licensecc-by-nc-4.0
    Downloads/mo75K
    Likes164

    Research Paper

    Depth Anything V2

    arxiv.org

    Build a pipeline with Depth-Anything-V2-Large

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