File size: 2,453 Bytes
bbe1beb e0cd76a bbe1beb e0cd76a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | ---
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}
}
```
|