Update README.md

README.md

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----
-license: agpl-3.0
----
+---
+license: agpl-3.0
+tags:
+  - image-segmentation
+  - background-removal
+  - onnx
+  - browser
+  - clip-art
+  - print-on-demand
+  - t-shirt-design
+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.**
+
+Both models trained at **1024×1024** on design-specific content (not photos). Exported as INT8-quantized ONNX for browser deployment via [ONNX Runtime Web](https://onnxruntime.ai/).
+
+## Models
+
+| File | Architecture | Size | WASM (CPU) | WebGL (GPU) |
+|------|-------------|------|------------|-------------|
+| `onnx/crispcut-fast.onnx` | MobileNetV2 + UNet (distilled) | **6.5 MB** | ~5–10s | ~1–2s |
+| `onnx/crispcut-quality.onnx` | EfficientNet-b5 + UNet++ + SCSE | **30.8 MB** | ~25–45s | ~5–10s |
+
+Both models:
+- Trained at **1024×1024** resolution
+- INT8 dynamic quantization
+- 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)
+
+## Usage with the npm package
+
+```bash
+npm install @crispcut/background-removal onnxruntime-web
+```
+
+```js
+import { removeBackground } from '@crispcut/background-removal';
+
+// Fast mode (default) — downloads crispcut-fast.onnx from this repo
+const result = await removeBackground(file);
+img.src = result.url;
+
+// Quality mode with GPU
+const result = await removeBackground(file, {
+  quality: 'quality',
+  executionProvider: 'webgl',
+});
+```
+
+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
+removeBackground(file, {
+  modelUrl: '/models/crispcut-fast.onnx',
+});
+```
+
+## Training Details
+
+- **Dataset:** Design-specific content (clip art, illustrations, t-shirt graphics, POD assets)
+- **Resolution:** 1024×1024
+- **Fast model:** Distilled from the quality model using knowledge distillation
+- **Quality model:** Full EfficientNet-b5 encoder with UNet++ decoder and SCSE attention
+- **Quantization:** INT8 dynamic (via ONNX Runtime)
+
+## 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)