HomeModelsDepth EstimationLloydAI/DepthAnything_v2-Small-CoreML
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LloydAI/DepthAnything_v2-Small-CoreML

Depth Estimation·LloydAI· 6· 2
coreml apache-2.0 Depth Estimation arxiv:2406.09414base_model:depth-anything/Depth-Anything-V2-Smallbase_model:quantized:depth-anything/Depth-Anything-V2-Smalllicense:apache-2.0region:us

In this repo you can find: The notebook which was used to convert depth-anything/Depth-Anything-V2-Small into a CoreML package. Both mlpackage files which can be opened in Xcode and used for Preview (of both images and videos) and development of macOS and iOS Apps Performence and compute unit mapping report for these models as meassured on an iPhone 16 Pro Max One model uses internal resolutio

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# pull & run locally
pip install mlforge-sdk && mlforge pull LloydAI/DepthAnything_v2-Small-CoreML

Model details

Task
Depth Estimation
Provider
LloydAI
Framework
coreml
Size
99 MB
License
apache-2.0
Downloads
6
Likes
2
Paper
arXiv:2406.09414
Updated
2025-03-18

About LloydAI/DepthAnything_v2-Small-CoreML

In this repo you can find: The notebook which was used to convert depth-anything/Depth-Anything-V2-Small into a CoreML package. Both mlpackage files which can be opened in Xcode and used for Preview (of both images and videos) and development of macOS and iOS Apps Performence and compute unit mapping report for these models as meassured on an iPhone 16 Pro Max One model uses internal resolution of 518x518 ("Box") and the other 518x392 ("Landscape"). The "Landscape" is much faster than "Box" but will also give more "juggy" edges, due to the patch I applied to avoid bicubing upsampling (.diff file is also present in this repo)

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