README.md

---
license: apache-2.0
language: en
tags:
- coreml
- u2net
- image-segmentation
- salient-object-detection
- macos
---

# U-2-Net (U2NET) - Core ML Format

U-2-Net model converted to Apple Core ML format for macOS GPU-accelerated inference.

## 📖 Original Model

- **Source Repository**: https://github.com/xuebinqin/U-2-Net
- **License**: Apache 2.0
- **Paper**: [U²-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://arxiv.org/abs/2005.09007)
- **Authors**: Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaïane, Martin Jagersand

## 🔄 Conversion Details

This model was converted from PyTorch to Core ML format using the following method:

- **Conversion Method**: `torch.jit.trace` → `coremltools.convert()`
- **Input Shape**: `(1, 3, 320, 320)` RGB image
- **Precision**: FP32
- **Compute Units**: CPU + GPU
- **Output Format**: Core ML Package (`.mlpackage`)

### Conversion Script

```python
import torch
import coremltools as ct
from u2net import U2NET

# Load model
u2net = U2NET(3, 1)
u2net.load_state_dict(torch.load("u2net.pth", map_location="cpu"))
u2net.eval()

# Trace and convert
dummy = torch.randn(1, 3, 320, 320)
traced = torch.jit.trace(u2net, dummy)
coreml_model = ct.convert(traced, inputs=[ct.ImageType(shape=(1, 3, 320, 320))])
coreml_model.save("U2NET.mlpackage")
```

## 📥 Usage

Load the model in a macOS application using Core ML:

```swift
import CoreML
import Vision

let config = MLModelConfiguration()
config.computeUnits = .cpuAndGPU

let model = try U2NET(configuration: config)
```

## 📊 Model Inputs/Outputs

**Input**:
- `image`: 3-channel RGB image, 320×320 pixels

**Outputs** (7 saliency maps at different scales):
- 7 output tensors, each 1-channel, 320×320

## ⚖️ License & Attribution

This repository contains a format conversion of the original U-2-Net model. All rights and credit belong to the original authors.

- **Original License**: Apache 2.0
- **Conversion**: Format-only, no weight modifications

## 📚 Citation

If you use this model, please cite the original paper:

```bibtex
@article{qin2020u2net,
  title={U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection},
  author={Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood and Zaiane, Osmar R and Jagersand, Martin},
  journal={Pattern Recognition},
  volume={106},
  pages={107404},
  year={2020},
  publisher={Elsevier}
}
```