Update README.md

README.md

@@ -5,7 +5,9 @@ tags:
  - portrait-matting
 ---
 
-https://github.com/ZHKKKe/MODNet with ONNX weights to be compatible with Transformers.js.
+# MODNet: Trimap-Free Portrait Matting in Real Time
+
+For more information, see the original [repo](https://github.com/ZHKKKe/MODNet) and example [colab](https://colab.research.google.com/drive/1P3cWtg8fnmu9karZHYDAtmm1vj1rgA-f?usp=sharing).
 
 ## Usage (Transformers.js)
 
@@ -17,11 +19,11 @@ npm i @xenova/transformers
 You can then use the model for portrait matting, as follows:
 
 ```js
-import { AutoProcessor, RawImage, AutoModel } from '@xenova/transformers';
+import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';
 
 // Load model and processor
-const model = await AutoModel.from_pretrained('Xenova/modnet-onnx', { quantized: false });
-const processor = await AutoProcessor.from_pretrained('Xenova/modnet-onnx');
+const model = await AutoModel.from_pretrained('Xenova/modnet', { quantized: false });
+const processor = await AutoProcessor.from_pretrained('Xenova/modnet');
 
 // Load image from URL
 const url = 'https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024';
@@ -45,8 +47,3 @@ mask.save('mask.png');
 ---
 
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
-
-
-
-
-For more information, see the original [repo](https://github.com/ZHKKKe/MODNet) and example [colab](https://colab.research.google.com/drive/1P3cWtg8fnmu9karZHYDAtmm1vj1rgA-f?usp=sharing).