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depth estimation by depth-anything

Depth-Anything-V2-Small-hf

Depth Anything V2 Small is a lightweight monocular depth estimation model trained on synthetic photorealistic data with fine-grained depth labels. V2 improves over V1 by using synthetic training data to reduce depth estimation errors in fine-grained regions.

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From the model card

Fields below are copied from the tags and counters on the HuggingFace repository depth-anything/Depth-Anything-V2-Small-hf at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
depth-anything
Pipeline tag
depth-estimation
Library
Transformers
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Papers cited
arXiv:2406.09414, arXiv:2401.10891
Downloads (HF counter at last fetch)
1,688,778
Likes (HF counter at last fetch)
46
Model card
https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf

Use cases

  • Real-time monocular depth estimation in applications
  • Depth map generation for 3D reconstruction pipelines
  • Obstacle awareness for autonomous navigation
  • Visual effects requiring depth-conditioned processing

Pros

  • Small variant enables fast CPU and mobile GPU inference
  • V2 synthetic training improves fine-grained depth accuracy over V1
  • Apache-2.0 licensed
  • HuggingFace-converted format for easy pipeline integration

Cons

  • Monocular depth is inherently scale-ambiguous without calibration
  • Small model trails Depth Anything V2 Base and Large on detailed scenes
  • Not suitable as a replacement for LiDAR in safety-critical applications
  • Performance degrades on transparent surfaces and mirrors

Tags

transformerssafetensorsdepth_anythingdepth-estimationdepthrelative deptharxiv:2406.09414arxiv:2401.10891license:apache-2.0endpoints_compatibleregion:us