fix readme.md

README.md

@@ -1,21 +1,76 @@
 ---
 pipeline_tag: image-segmentation
 license: cc-by-nc-4.0
+base_model: facebook/maskformer-swin-tiny-coco
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
 - maskformer
-- tf
-- jax
+- universal-segmentation
+- image-segmentation
+- arxiv:2107.06278
 - pytorch
+- jax
+- tf
 ---
 
-# maskformer-swin-tiny-coco (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/maskformer-6a6a8ece1c77558c676dfb9d) for all versions of MaskFormer.***
+
+# Run MaskFormer 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-MaskFormer-blue)](https://imvision12.github.io/KerasFormers/maskformer/) [![Collection](https://img.shields.io/badge/HF-MaskFormer%20collection-yellow)](https://huggingface.co/collections/kerasformers/maskformer-6a6a8ece1c77558c676dfb9d)
+
+# kerasformers/maskformer-swin-tiny-coco
+
+Paper: [Per-Pixel Classification is Not All You Need for Semantic Segmentation (arXiv:2107.06278)](https://arxiv.org/abs/2107.06278) · [HF Papers](https://huggingface.co/papers/2107.06278)
+
+MaskFormer reframes segmentation as mask classification: a backbone and pixel decoder feed a transformer decoder whose queries each predict a binary mask and a class. One architecture covers semantic, instance, and panoptic outputs via post-processing.\n
+For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/maskformer-swin-tiny-coco).
+
+Pure-**Keras 3** conversion of [`facebook/maskformer-swin-tiny-coco`](https://huggingface.co/facebook/maskformer-swin-tiny-coco) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **universal segmentation** checkpoint (`MaskFormerUniversalSegment`) trained on COCO panoptic.
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the source. License: `cc-by-nc-4.0`.
+## ✨ Quick start
 
 ```python
-from kerasformers.models.maskformer import MaskFormerUniversalSegment
-model = MaskFormerUniversalSegment.from_weights("maskformer-swin-tiny-coco")
+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"]}.from_weights("kerasformers/{variant}")
+
+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 MaskFormer variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Dataset |
+|---|---|---|
+| `maskformer-swin-tiny-coco` | [`kerasformers/maskformer-swin-tiny-coco`](https://huggingface.co/kerasformers/maskformer-swin-tiny-coco) | COCO |
+| `maskformer-swin-small-coco` | [`kerasformers/maskformer-swin-small-coco`](https://huggingface.co/kerasformers/maskformer-swin-small-coco) | COCO |
+| `maskformer-swin-base-coco` | [`kerasformers/maskformer-swin-base-coco`](https://huggingface.co/kerasformers/maskformer-swin-base-coco) | COCO |
+| `maskformer-swin-tiny-ade` | [`kerasformers/maskformer-swin-tiny-ade`](https://huggingface.co/kerasformers/maskformer-swin-tiny-ade) | ADE20K |
+| `maskformer-swin-base-ade` | [`kerasformers/maskformer-swin-base-ade`](https://huggingface.co/kerasformers/maskformer-swin-base-ade) | ADE20K |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `MaskFormerImageProcessor.from_weights(...)` so resolution matches the variant.
+- See [MaskFormer docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream weights: `MaskFormerUniversalSegment.from_weights("hf:facebook/maskformer-swin-tiny-coco")`.
+
+## Special Thanks
+
+A huge thank you to the Facebook AI Research MaskFormer authors for creating and releasing these models.
+
+License: CC-BY-NC-4.0 (non-commercial).