Distilled from BiRefNet (220 M params → 6.6 M params) with ~95 % quality retention. Exported as ONNX for browser deployment via ONNX Runtime Web.

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

npm i @crispcut/background-removal

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 💻 GitHub: bowespublishing/crispcut

Self-hosting

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

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

bowespublishing/crisp-cut

✂️ CrispCut — AI Background Removal for Designs