fix readme.md

README.md

@@ -1,21 +1,77 @@
 ---
 pipeline_tag: image-segmentation
 license: mit
+base_model: facebook/mask2former-swin-tiny-coco-instance
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
 - mask2former
-- tf
-- jax
+- instance-segmentation
+- image-segmentation
+- arxiv:2112.01527
 - pytorch
+- jax
+- tf
 ---
 
-# mask2former-swin-tiny-coco-instance (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/mask2former-6a6a8f4248526cc9a74b9b97) for all versions of Mask2Former.***
+
+# Run Mask2Former with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-Mask2Former-blue)](https://imvision12.github.io/KerasFormers/mask2former/) [![Collection](https://img.shields.io/badge/HF-Mask2Former%20collection-yellow)](https://huggingface.co/collections/kerasformers/mask2former-6a6a8f4248526cc9a74b9b97)
+
+# kerasformers/mask2former-swin-tiny-coco-instance
+
+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
+For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/mask2former-swin-tiny-coco-instance).
+
+Pure-**Keras 3** conversion of [`facebook/mask2former-swin-tiny-coco-instance`](https://huggingface.co/facebook/mask2former-swin-tiny-coco-instance) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **instance** checkpoint (`Mask2FormerUniversalSegment`) (trained for instance; architecture is universal).
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the source. License: `mit`.
+## ✨ Quick start
 
 ```python
-from kerasformers.models.mask2former import Mask2FormerUniversalSegment
-model = Mask2FormerUniversalSegment.from_weights("mask2former-swin-tiny-coco-instance")
+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']}
+
+model = {meta["load_cls"]}.from_weights("kerasformers/{variant}")
+processor = {meta['proc_cls']}()
+
+image = Image.open("your_image.jpg").convert("RGB")
+output = model(processor(image)["pixel_values"], training=False)
+result = processor.post_process_panoptic_segmentation(
+    output, target_size=(image.height, image.width)
+)
+print(result["segmentation"].shape)
 ```
+
+Load any Mask2Former variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Task |
+|---|---|---|
+| `mask2former-swin-tiny-coco-instance` | [`kerasformers/mask2former-swin-tiny-coco-instance`](https://huggingface.co/kerasformers/mask2former-swin-tiny-coco-instance) | instance |
+| `mask2former-swin-small-coco-instance` | [`kerasformers/mask2former-swin-small-coco-instance`](https://huggingface.co/kerasformers/mask2former-swin-small-coco-instance) | instance |
+| `mask2former-swin-base-coco-instance` | [`kerasformers/mask2former-swin-base-coco-instance`](https://huggingface.co/kerasformers/mask2former-swin-base-coco-instance) | instance |
+| `mask2former-swin-large-coco-instance` | [`kerasformers/mask2former-swin-large-coco-instance`](https://huggingface.co/kerasformers/mask2former-swin-large-coco-instance) | instance |
+| `mask2former-swin-tiny-coco-panoptic` | [`kerasformers/mask2former-swin-tiny-coco-panoptic`](https://huggingface.co/kerasformers/mask2former-swin-tiny-coco-panoptic) | panoptic |
+| `mask2former-swin-tiny-ade-semantic` | [`kerasformers/mask2former-swin-tiny-ade-semantic`](https://huggingface.co/kerasformers/mask2former-swin-tiny-ade-semantic) | semantic |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- The task suffix is what the checkpoint was trained for; post-process accordingly.
+- See [Mask2Former docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream weights: `Mask2FormerUniversalSegment.from_weights("hf:facebook/mask2former-swin-tiny-coco-instance")`.
+
+## Special Thanks
+
+A huge thank you to the Facebook AI Research Mask2Former authors for creating and releasing these models.
+
+License: MIT.