---
license: mit
language:
- en
pipeline_tag: depth-estimation
tags:
- coreml
---

Based on https://github.com/fabiotosi92/ZipDepth - Converted to CoreML for use on iPhone Devices, Internal testing suggested iPhone 12> will attain good results

📊 Quantitative Results

ZipDepth achieves state-of-the-art accuracy among lightweight embedded models on NYUv2, KITTI, ETH3D, ScanNet, and DIODE, while being significantly more efficient than large pretrained models.

![image](https://cdn-uploads.huggingface.co/production/uploads/682c33f86f59c83933a1f1df/MkETb44ehq4_CYAoA-zbh.png)

🏗️ Architecture


![image](https://cdn-uploads.huggingface.co/production/uploads/682c33f86f59c83933a1f1df/orDKivbQj0SBw3uLS2_8J.png)

The encoder is organized in four hierarchical stages. Stages 1–2 use RepVGG reparameterizable blocks (3×3 + 1×1 + identity branches fused into a single 3×3 at inference) augmented with Strip Pooling Attention for horizontal/vertical context. Stage 3 adds Squeeze-and-Excitation channel attention and a Global Context Block. Stage 4 deepens the representation with additional RepVGG blocks.

The neck combines SPPF multi-scale pooling with a Cross-Scale Fusion module. The decoder is a lightweight FPN with a Convex Upsampling head for sub-pixel-accurate depth maps.