fix readme.md

README.md

@@ -25,7 +25,8 @@ tags:
 
 Paper: [Masked-attention Mask Transformer for Universal Image Segmentation (arXiv:2112.01527)](https://arxiv.org/abs/2112.01527) · [HF Papers](https://huggingface.co/papers/2112.01527)
 
-Mask2Former improves MaskFormer with masked attention in the transformer decoder, restricting cross-attention to predicted mask regions for sharper boundaries and stronger universal segmentation.\n
+Mask2Former improves MaskFormer with masked attention in the transformer decoder, restricting cross-attention to predicted mask regions for sharper boundaries and stronger universal segmentation.
+
 For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic).
 
 Pure-**Keras 3** conversion of [`facebook/mask2former-swin-tiny-coco-panoptic`](https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
@@ -39,10 +40,10 @@ import os
 os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
 
 from PIL import Image
-from {meta['import_path']} import {meta['load_cls']}, {meta['proc_cls']}
+from kerasformers.models.mask2former import Mask2FormerUniversalSegment, Mask2FormerImageProcessor
 
-model = {meta["load_cls"]}.from_weights("kerasformers/{variant}")
-processor = {meta['proc_cls']}()
+model = Mask2FormerUniversalSegment.from_weights("kerasformers/mask2former-swin-tiny-coco-panoptic")
+processor = Mask2FormerImageProcessor()
 
 image = Image.open("your_image.jpg").convert("RGB")
 output = model(processor(image)["pixel_values"], training=False)