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apple/coreml-depth-anything-v2-small

Depth Estimation·apple· 792· 100
coreml apache-2.0 Depth Estimation arxiv:2406.09414arxiv:2401.10891license:apache-2.0region:us

Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lih

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# pull & run locally
pip install mlforge-sdk && mlforge pull apple/coreml-depth-anything-v2-small

Model details

Task
Depth Estimation
Provider
apple
Framework
coreml
Size
579 MB
License
apache-2.0
Downloads
792
Likes
100
Paper
arXiv:2406.09414
Updated
2024-06-24

About apple/coreml-depth-anything-v2-small

Depth Anything V2 was introduced in the paper of the same name by Lihe Yang et al. It uses the same architecture as the original Depth Anything release, but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions. The original Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al., and was first released in this repository.

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