Depth-Anything-V2-Large
by depth-anything
Foundation model for monocular depth estimation with synthetic-to-real training
depth-anything/Depth-Anything-V2-Largemixpeek://image_extractor@v1/depth_anything_v2_large_v1Overview
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
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| NYUv2 | AbsRel | 0.043 | Yang et al., 2024: Depth Anything V2 paper |
| KITTI | AbsRel | 0.044 | Yang et al., 2024: Depth Anything V2 paper |
| Sintel | AbsRel | 0.280 | Yang et al., 2024: Depth Anything V2 paper |
Performance
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Specification
Research Paper
Depth Anything V2
arxiv.orgBuild a pipeline with Depth-Anything-V2-Large
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