docs(README): Add MODNet model usage instructions and example code

- Added complete example code for portrait segmentation using Transformers.js
- Displayed example results of input images and output masks

README.md

@@ -10,4 +10,84 @@ pipeline_tag: image-segmentation
 
 # wuchendi/MODNet (Matting Objective Decomposition Network)
 
-Trimap-Free Portrait Matting in Real Time
+> Trimap-Free Portrait Matting in Real Time
+
+- Repository: <https://huggingface.co/wuchendi/MODNet>
+- SwanLab/MODNet: <https://swanlab.cn/@wudi/MODNet/overview>
+
+### 📦 Usage with [Transformers.js](https://www.npmjs.com/package/@huggingface/transformers)
+
+First, install the `@huggingface/transformers` library from NPM:
+
+```bash
+pnpm add @huggingface/transformers
+```
+
+Then, use the following code to perform **portrait matting** with the `wuchendi/MODNet` model:
+
+```ts
+/* eslint-disable no-console */
+import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers'
+
+async function main() {
+  try {
+    console.log('🚀 Initializing MODNet...')
+
+    // Load model
+    console.log('📦 Loading model...')
+    const model = await AutoModel.from_pretrained('wuchendi/MODNet', {
+      dtype: 'fp32',
+      progress_callback: (progress) => {
+        // @ts-ignore
+        if (progress.progress) {
+          // @ts-ignore
+          console.log(`Model loading progress: ${(progress.progress).toFixed(2)}%`)
+        }
+      }
+    })
+    console.log('✅ Model loaded successfully')
+
+    // Load processor
+    console.log('🔧 Loading processor...')
+    const processor = await AutoProcessor.from_pretrained('wuchendi/MODNet', {})
+    console.log('✅ Processor loaded successfully')
+
+    // Load image from URL
+    const url = 'https://res.cloudinary.com/dhzm2rp05/image/upload/samples/logo.jpg'
+    console.log('🖼️ Loading image:', url)
+    const image = await RawImage.fromURL(url)
+    console.log('✅ Image loaded successfully', `Dimensions: ${image.width}x${image.height}`)
+
+    // Pre-process image
+    console.log('🔄 Preprocessing image...')
+    const { pixel_values } = await processor(image)
+    console.log('✅ Image preprocessing completed')
+
+    // Generate alpha matte
+    console.log('🎯 Generating alpha matte...')
+    const startTime = performance.now()
+    const { output } = await model({ input: pixel_values })
+    const inferenceTime = performance.now() - startTime
+    console.log('✅ Alpha matte generated', `Time: ${inferenceTime.toFixed(2)}ms`)
+
+    // Save output mask
+    console.log('💾 Saving output...')
+    const mask = await RawImage.fromTensor(output[0].mul(255).to('uint8')).resize(image.width, image.height)
+    await mask.save('src/assets/mask.png')
+    console.log('✅ Output saved to assets/mask.png')
+
+  } catch (error) {
+    console.error('❌ Error during processing:', error)
+    throw error
+  }
+}
+
+main().catch(console.error)
+
+```
+
+### 🖼️ Example Result
+
+| Input Image                         | Output Mask                        |
+| ----------------------------------- | ---------------------------------- |
+| ![](https://cdn.jsdelivr.net/gh/WuChenDi/MODNet/examples/src/assets/Input.jpg) | ![](https://cdn.jsdelivr.net/gh/WuChenDi/MODNet/examples/src/assets/mask.png) |