Post
2315
🔁 One API. 12 model families. 28 variants. Why depth_estimation makes depth research easier
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch
Switching between depth models usually means rewriting preprocessing, adapting outputs, and dealing with different codebases.
depth_estimation removes that friction.
With the same interface, you can work with:
🌊 Depth Anything
🍎 DepthPro
🧭 MiDaS
📏 ZoeDepth
🧩 MoGe
🛰️ VGGT / OmniVGGT
and more
Change one model string, keep the rest of your workflow the same.
That makes it much easier to:
⚖️ compare models fairly
🧪 prototype quickly
📈 benchmark consistently
🛠️ build reusable depth pipelines
GitHub: https://github.com/shriarul5273/depth_estimation
#depthestimation #research #computervision #python #machinelearning #opensource #pytorch