Update README.md with new model card content

README.md

@@ -1,25 +1,109 @@
 ---
 library_name: keras-hub
 ---
-This is a [`SwinTransformer` model](https://keras.io/api/keras_hub/models/swin_transformer) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-This model is related to a `ImageClassifier` task.
-
-Model config:
-* **name:** swin_transformer_backbone
-* **trainable:** True
-* **dtype:** {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
-* **image_shape:** [384, 384, 3]
-* **patch_size:** 4
-* **embed_dim:** 128
-* **depths:** [2, 2, 18, 2]
-* **num_heads:** [4, 8, 16, 32]
-* **window_size:** 12
-* **mlp_ratio:** 4.0
-* **qkv_bias:** True
-* **dropout_rate:** 0.0
-* **attention_dropout:** 0.0
-* **drop_path:** 0.1
-* **patch_norm:** True
-* **data_format:** channels_last
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+# Swin Transformer
+
+Instantiates the Swin Transformer architecture.
+
+## Model Details
+
+The Swin Transformer (Shifted Window Transformer) is a hierarchical vision transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.
+
+This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities make Swin Transformer compatible with a broad range of vision tasks, including image classification, object detection, and semantic segmentation.
+
+### Reference
+* [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+
+Unlike traditional Vision Transformers (ViT), which compute attention globally across all patches (resulting in quadratic complexity relative to image size), Swin Transformer computes self-attention within local non-overlapping windows. By shifting the window partition between consecutive layers, the model achieves cross-window connections, maintaining linear computational complexity while enabling robust global context modeling.
+
+### Links
+* [Swin Transformer Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/swin-transformer-quickstart-keras-hub) 
+* [Swin Transformer API Documentation](https://keras.io/keras_hub/api/models/swin_transformer/)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+## Installation
+
+Keras and KerasHub can be installed with:
+```bash
+pip install -U -q keras-hub
+pip install -U -q keras
+```
+
+JAX, TensorFlow, and PyTorch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment, see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+The following model checkpoints are provided by the Keras team. Weights have been ported from [Hugging Face Hub](https://huggingface.co/microsoft).
+
+| Preset name | Parameters | Description |
+| :--- | :--- | :--- |
+| **swin_tiny_patch4_window7_224** | 28.29M | Tiny Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
+| **swin_small_patch4_window7_224** | 49.61M | Small Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
+| **swin_base_patch4_window7_224** | 87.77M | Base Swin Transformer model pre-trained on ImageNet-1k at a 224x224 resolution |
+| **swin_base_patch4_window12_384** | 87.90M | Base Swin Transformer model pre-trained on ImageNet-1k at a 384x384 resolution |
+| **swin_large_patch4_window7_224** | 196.53M | Large Swin Transformer model pre-trained ImageNet-1k at a 224x224 resolution |
+| **swin_large_patch4_window12_384** | 196.74M | Large Swin Transformer model pre-trained on ImageNet-1k at a 384x384 resolution |
+
+## Example Use
+
+```python
+import numpy as np
+import keras_hub
+
+# Pretrained Swin Transformer backbone
+model = keras_hub.models.SwinTransformerBackbone.from_preset("swin_tiny_patch4_window7_224")
+input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
+model(input_data)
+
+# Randomly initialized Swin Transformer backbone with custom config
+model = keras_hub.models.SwinTransformerBackbone(
+    image_shape=(224, 224, 3),
+    embed_dim=96,
+    depths=(2, 2, 6, 2),
+    num_heads=(3, 6, 12, 24),
+    window_size=7,
+)
+model(input_data)
+
+# Use Swin Transformer for image classification task
+classifier = keras_hub.models.SwinTransformerImageClassifier.from_preset(
+    "swin_tiny_patch4_window7_224",
+    num_classes=1000,
+)
+
+# Use Hugging Face presets directly for on-the-fly conversion
+classifier = keras_hub.models.SwinTransformerImageClassifier.from_preset(
+    "hf://microsoft/swin-tiny-patch4-window7-224"
+)
+```
+
+## Example Usage
+```
+import numpy as np
+import keras_hub
+
+# Top-5 ImageNet class decoding.
+model = keras_hub.models.SwinTransformerImageClassifier.from_preset(
+    "swin_base_patch4_window12_384"
+)
+images = np.random.randint(0, 256, size=(1, 384, 384, 3), dtype="uint8")
+logits = model.predict(images, verbose=0)
+print(keras_hub.utils.decode_imagenet_predictions(logits, top=5)[0])
+```
+
+## Example Usage with Hugging Face URI
+
+```
+import numpy as np
+import keras_hub
+
+# Top-5 ImageNet class decoding.
+model = keras_hub.models.SwinTransformerImageClassifier.from_preset(
+    "hf://keras/swin_base_patch4_window12_384"
+)
+images = np.random.randint(0, 256, size=(1, 384, 384, 3), dtype="uint8")
+logits = model.predict(images, verbose=0)
+print(keras_hub.utils.decode_imagenet_predictions(logits, top=5)[0])
+```