Update README.md with new model card content

README.md

@@ -1,19 +1,93 @@
 ---
 library_name: keras-hub
 ---
-This is a [`ResNet` model](https://keras.io/api/keras_hub/models/res_net) 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:** res_net_backbone
-* **trainable:** True
-* **input_conv_filters:** [64]
-* **input_conv_kernel_sizes:** [7]
-* **stackwise_num_filters:** [64, 128, 256, 512]
-* **stackwise_num_blocks:** [2, 2, 2, 2]
-* **stackwise_num_strides:** [1, 2, 2, 2]
-* **block_type:** basic_block
-* **use_pre_activation:** False
-* **image_shape:** [None, None, 3]
-
-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
+Instantiates the ResNet architecture. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
+
+**Reference**
+
+- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
+
+The difference in ResNetV1 and ResNetV2 rests in the structure of their
+individual building blocks. In ResNetV2, the batch normalization and
+ReLU activation precede the convolution layers, as opposed to ResNetV1 where
+the batch normalization and ReLU activation are applied after the
+convolution layers.
+
+## Links
+
+coming soon
+
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras
+```
+
+Jax, TensorFlow, and Torch 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: https://huggingface.co/timm. Full code examples for each are available below.
+
+| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
+|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| resnet_18_imagenet | 11.19M      | 18-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution |
+| resnet_50_imagenet | 23.56M      | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution |
+| resnet_101_imagenet | 42.61M      | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution |
+| resnet_152_imagenet | 58.30M      | 52-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution |
+
+### Example Usage
+```python
+    # Pretrained ResNet backbone.
+    model = keras_hub.models.ResNetBackbone.from_preset("resnet_101_imagenet")
+    input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
+    model(input_data)
+
+    # Randomly initialized ResNetV2 backbone with a custom config.
+    model = keras_hub.models.ResNetBackbone(
+        input_conv_filters=[64],
+        input_conv_kernel_sizes=[7],
+        stackwise_num_filters=[64, 64, 64],
+        stackwise_num_blocks=[2, 2, 2],
+        stackwise_num_strides=[1, 2, 2],
+        block_type="basic_block",
+        use_pre_activation=True,
+    )
+    model(input_data)
+    # Use resnet for image classification task
+    model = keras_hub.models.ImageClassifier.from_preset("resnet_101_imagenet")
+
+    # User timm presets directly from hugingface
+    model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnet101.a1_in1k')
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+    # Pretrained ResNet backbone.
+    model = keras_hub.models.ResNetBackbone.from_preset("resnet_101_imagenet")
+    input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
+    model(input_data)
+
+    # Randomly initialized ResNetV2 backbone with a custom config.
+    model = keras_hub.models.ResNetBackbone(
+        input_conv_filters=[64],
+        input_conv_kernel_sizes=[7],
+        stackwise_num_filters=[64, 64, 64],
+        stackwise_num_blocks=[2, 2, 2],
+        stackwise_num_strides=[1, 2, 2],
+        block_type="basic_block",
+        use_pre_activation=True,
+    )
+    model(input_data)
+    # Use resnet for image classification task
+    model = keras_hub.models.ImageClassifier.from_preset("resnet_101_imagenet")
+
+    # User timm presets directly from hugingface
+    model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnet101.a1_in1k')
+```