fix readme.md

README.md

@@ -8,20 +8,78 @@ tags:
 - kerasformers
 - image-classification
 - flexivit
-- tf
-- jax
+- backbone
+- arxiv:2212.08013
 - pytorch
+- jax
+- tf
 ---
 
-# flexivit_large_600ep_in1k
+## ***See [our collection](https://huggingface.co/collections/kerasformers/flexivit-6a6bd6145b07dd08e5e51814) for all versions of FlexiViT.***
+
+# Run FlexiViT 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-FlexiViT-blue)](https://imvision12.github.io/KerasFormers/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-FlexiViT%20collection-yellow)](https://huggingface.co/collections/kerasformers/flexivit-6a6bd6145b07dd08e5e51814)
+
+# kerasformers/flexivit_large_600ep_in1k
+
+Paper: [FlexiViT: One Model for All Patch Sizes (arXiv:2212.08013)](https://arxiv.org/abs/2212.08013) · [HF Papers](https://huggingface.co/papers/2212.08013)
+
+FlexiViT trains one ViT that transfers across patch sizes at inference. Classifier or token backbone.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/flexivit_large.600ep_in1k).
 
-Pure-Keras 3 image-classification weight for [kerasformers](https://github.com/IMvision12/KerasFormers), converted from [timm/flexivit_large.600ep_in1k](https://huggingface.co/timm/flexivit_large.600ep_in1k).
+Pure-**Keras 3** conversion of [`timm/flexivit_large.600ep_in1k`](https://huggingface.co/timm/flexivit_large.600ep_in1k) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 
-## Usage
+This is an **image-classification / backbone** checkpoint (`FlexiViTImageClassify` / `FlexiViTModel`).
+
+## ✨ Quick start
 
 ```python
-from kerasformers.models.flexivit import FlexiViTImageClassify
-model = FlexiViTImageClassify.from_weights("flexivit_large_600ep_in1k")
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+import numpy as np
+from kerasformers.models.flexivit import FlexiViTImageClassify, FlexiViTModel
+
+model = FlexiViTImageClassify.from_weights("kerasformers/flexivit_large_600ep_in1k")
+backbone = FlexiViTModel.from_weights(
+    "kerasformers/flexivit_large_600ep_in1k", as_backbone=True
+)
+
+image = Image.open("your_image.jpg").convert("RGB")
+image = image.resize((224, 224))
+x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
+print(model(x).shape)  # (1, num_classes)
+feats = backbone(x)
+print(len(feats), [tuple(f.shape) for f in feats])
 ```
 
-License: **apache-2.0**, inherited from the upstream source.
+Load any FlexiViT variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `flexivit_base_1000ep_in21k` | [`kerasformers/flexivit_base_1000ep_in21k`](https://huggingface.co/kerasformers/flexivit_base_1000ep_in21k) |
+| `flexivit_base_1200ep_in1k` | [`kerasformers/flexivit_base_1200ep_in1k`](https://huggingface.co/kerasformers/flexivit_base_1200ep_in1k) |
+| `flexivit_base_300ep_in1k` | [`kerasformers/flexivit_base_300ep_in1k`](https://huggingface.co/kerasformers/flexivit_base_300ep_in1k) |
+| `flexivit_base_300ep_in21k` | [`kerasformers/flexivit_base_300ep_in21k`](https://huggingface.co/kerasformers/flexivit_base_300ep_in21k) |
+| `flexivit_large_1200ep_in1k` | [`kerasformers/flexivit_large_1200ep_in1k`](https://huggingface.co/kerasformers/flexivit_large_1200ep_in1k) |
+| `flexivit_large_300ep_in1k` | [`kerasformers/flexivit_large_300ep_in1k`](https://huggingface.co/kerasformers/flexivit_large_300ep_in1k) |
+| `flexivit_large_600ep_in1k` | [`kerasformers/flexivit_large_600ep_in1k`](https://huggingface.co/kerasformers/flexivit_large_600ep_in1k) |
+| `flexivit_small_1200ep_in1k` | [`kerasformers/flexivit_small_1200ep_in1k`](https://huggingface.co/kerasformers/flexivit_small_1200ep_in1k) |
+| `flexivit_small_300ep_in1k` | [`kerasformers/flexivit_small_300ep_in1k`](https://huggingface.co/kerasformers/flexivit_small_300ep_in1k) |
+| `flexivit_small_600ep_in1k` | [`kerasformers/flexivit_small_600ep_in1k`](https://huggingface.co/kerasformers/flexivit_small_600ep_in1k) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- `FlexiViTImageClassify` returns class logits; `FlexiViTModel` returns features (`as_backbone=True` for multi-scale stages).
+- See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Upstream / timm checkpoints: `FlexiViTImageClassify.from_weights("hf:timm/flexivit_large.600ep_in1k")`.
+
+## Special Thanks
+
+A huge thank you to the FlexiViT authors and the timm / Hub communities for creating and releasing these models.
+
+License: see YAML `license` (usually matches the upstream checkpoint).