---
license: agpl-3.0
tags:
  - image-segmentation
  - background-removal
  - onnx
  - browser
  - clip-art
  - print-on-demand
  - t-shirt-design
  - birefnet
  - knowledge-distillation
library_name: onnxruntime
pipeline_tag: image-segmentation
---

# ✂️ CrispCut — AI Background Removal for Designs

**Purpose-built background removal for clip art, t-shirt designs, and print-on-demand assets.**

Distilled from [**BiRefNet**](https://github.com/ZhengPeng7/BiRefNet) (220 M params → 6.6 M params) with **~95 % quality retention**. Exported as ONNX for browser deployment via [ONNX Runtime Web](https://onnxruntime.ai/).

## Models

| File | Precision | Size | WASM (CPU) | WebGL (GPU) |
|------|-----------|------|------------|-------------|
| `onnx/crispcut-fast.onnx` | INT8 quantized | **6.5 MB** | ~5–10 s | ~1–2 s |
| `onnx/crispcut-quality.onnx` | FP32 | **25.3 MB** | ~15–25 s | ~3–6 s |

Both models:
- **Architecture:** MobileNetV2 + UNet (distilled from BiRefNet)
- **Trained at 1024×1024** on design-specific content
- ONNX opset 17
- ImageNet normalisation (mean: `[0.485, 0.456, 0.406]`, std: `[0.229, 0.224, 0.225]`)
- Single input tensor: `input` — shape `[1, 3, 1024, 1024]` (NCHW, float32)
- Single output tensor: `output` — shape `[1, 1, 1024, 1024]` (logits → apply sigmoid)

## Distillation Details

| | Teacher (BiRefNet) | Student (CrispCut) |
|---|---|---|
| Parameters | 220 M | 6.6 M |
| Compression | — | **33× smaller** |
| Quality | 100 % | ~95 % |

The student model uses a MobileNetV2 encoder with a UNet decoder, trained via knowledge distillation from the full BiRefNet teacher on design-specific data.

## Usage with the npm package

```bash
npm i @crispcut/background-removal
```

```js
import { cut } from '@crispcut/background-removal';

// Fast mode (default) — downloads crispcut-fast.onnx from this repo
const result = await cut(image);
img.src = result.url;

// Quality mode with GPU
const result = await cut(image, { model: 'quality', gpu: true });
```

Models are fetched automatically from this repo at runtime. No server needed — everything runs in the browser.

📦 **npm:** [@crispcut/background-removal](https://www.npmjs.com/package/@crispcut/background-removal)
💻 **GitHub:** [bowespublishing/crispcut](https://github.com/bowespublishing/crispcut)

## Self-hosting

Download the `.onnx` files from the `onnx/` folder and serve them from your own CDN:

```js
cut(image, { modelUrl: '/models/crispcut-fast.onnx' });
```

## Training Details

- **Teacher:** BiRefNet (220 M parameters)
- **Student:** MobileNetV2 + UNet (6.6 M parameters)
- **Dataset:** Design-specific content (clip art, illustrations, t-shirt graphics, POD assets)
- **Resolution:** 1024×1024
- **Distillation method:** Knowledge distillation with feature-level and output-level supervision
- **Fast model:** INT8 dynamic quantization (via ONNX Runtime)
- **Quality model:** Full FP32 precision

## License

**AGPL-3.0** for open-source and personal use.

**Commercial license** required for closed-source or commercial products.

📩 Contact: [bowespublishing@gmail.com](mailto:bowespublishing@gmail.com)