Update README.md with new model card content

README.md

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 ---
 library_name: keras-hub
 ---
-This is a [`RetinaNet` model](https://keras.io/api/keras_hub/models/retina_net) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-This model is related to a `ObjectDetector` task.
-
-Model config:
-* **name:** retina_net_backbone_1
-* **trainable:** True
-* **image_encoder:** {'module': 'keras_hub.src.models.resnet.resnet_backbone', 'class_name': 'ResNetBackbone', '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': [3, 4, 6, 3], 'stackwise_num_strides': [1, 2, 2, 2], 'block_type': 'bottleneck_block', 'use_pre_activation': False, 'image_shape': [None, None, 3]}, 'registered_name': 'keras_hub>ResNetBackbone'}
-* **min_level:** 3
-* **max_level:** 7
-* **use_p5:** False
-* **use_fpn_batch_norm:** 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
+A Keras model implementing the RetinaNet meta-architecture.
+
+Implements the RetinaNet architecture for object detection. The constructor
+requires `num_classes`, `bounding_box_format`, and a backbone. Optionally,
+a custom label encoder, and prediction decoder may be provided.
+
+
+__Arguments__
+
+
+- __num_classes__: the number of classes in your dataset excluding the
+    background class. Classes should be represented by integers in the
+    range [0, num_classes).
+- __bounding_box_format__: The format of bounding boxes of input dataset.
+    Refer
+    [to the keras.io docs](https://keras.io/api/keras_cv/bounding_box/formats/)
+    for more details on supported bounding box formats.
+- __backbone__: `keras.Model`. If the default `feature_pyramid` is used,
+    must implement the `pyramid_level_inputs` property with keys "P3", "P4",
+    and "P5" and layer names as values. A somewhat sensible backbone
+    to use in many cases is the:
+    `keras_cv.models.ResNetBackbone.from_preset("resnet50_imagenet")`
+- __anchor_generator__: (Optional) a `keras_cv.layers.AnchorGenerator`. If
+    provided, the anchor generator will be passed to both the
+    `label_encoder` and the `prediction_decoder`. Only to be used when
+    both `label_encoder` and `prediction_decoder` are both `None`.
+    Defaults to an anchor generator with the parameterization:
+    `strides=[2**i for i in range(3, 8)]`,
+    `scales=[2**x for x in [0, 1 / 3, 2 / 3]]`,
+    `sizes=[32.0, 64.0, 128.0, 256.0, 512.0]`,
+    and `aspect_ratios=[0.5, 1.0, 2.0]`.
+- __label_encoder__: (Optional) a keras.Layer that accepts an image Tensor, a
+    bounding box Tensor and a bounding box class Tensor to its `call()`
+    method, and returns RetinaNet training targets. By default, a
+    KerasCV standard `RetinaNetLabelEncoder` is created and used.
+    Results of this object's `call()` method are passed to the `loss`
+    object for `box_loss` and `classification_loss` the `y_true`
+    argument.
+- __prediction_decoder__: (Optional)  A `keras.layers.Layer` that is
+    responsible for transforming RetinaNet predictions into usable
+    bounding box Tensors. If not provided, a default is provided. The
+    default `prediction_decoder` layer is a
+    `keras_cv.layers.MultiClassNonMaxSuppression` layer, which uses
+    a Non-Max Suppression for box pruning.
+- __feature_pyramid__: (Optional) A `keras.layers.Layer` that produces
+    a list of 4D feature maps (batch dimension included)
+    when called on the pyramid-level outputs of the `backbone`.
+    If not provided, the reference implementation from the paper will be used.
+- __classification_head__: (Optional) A `keras.Layer` that performs
+    classification of the bounding boxes. If not provided, a simple
+    ConvNet with 3 layers will be used.
+- __box_head__: (Optional) A `keras.Layer` that performs regression of the
+    bounding boxes. If not provided, a simple ConvNet with 3 layers
+    will be used.
+
+## Example Usage
+## Pretrained RetinaNet model
+```
+object_detector = keras_hub.models.ImageObjectDetector.from_preset(
+    "retinanet_resnet50_fpn_coco"
+)
+
+input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
+object_detector(input_data)
+```
+
+## Fine-tune the pre-trained model
+```python3
+backbone = keras_hub.models.Backbone.from_preset(
+    "retinanet_resnet50_fpn_coco"
+)
+preprocessor = keras_hub.models.RetinaNetObjectDetectorPreprocessor.from_preset(
+    "retinanet_resnet50_fpn_coco"
+)
+model = RetinaNetObjectDetector(
+    backbone=backbone,
+    num_classes=len(CLASSES),
+    preprocessor=preprocessor
+)
+```
+
+## Custom training the model
+```python3
+image_converter = keras_hub.layers.RetinaNetImageConverter(
+    scale=1/255
+)
+
+preprocessor = keras_hub.models.RetinaNetObjectDetectorPreprocessor(
+    image_converter=image_converter
+)
+# Load a pre-trained ResNet50 model. 
+# This will serve as the base for extracting image features.
+image_encoder = keras_hub.models.Backbone.from_preset(
+    "resnet_50_imagenet" 
+)
+
+# Build the RetinaNet Feature Pyramid Network (FPN) on top of the ResNet50 
+# backbone. The FPN creates multi-scale feature maps for better object detection
+# at different sizes.
+backbone = keras_hub.models.RetinaNetBackbone(
+    image_encoder=image_encoder,
+    min_level=3,
+    max_level=5,
+    use_p5=False 
+)
+model = RetinaNetObjectDetector(
+    backbone=backbone,
+    num_classes=len(CLASSES),
+    preprocessor=preprocessor
+)
+```
+
+## Example Usage with Hugging Face URI
+
+## Pretrained RetinaNet model
+```
+object_detector = keras_hub.models.ImageObjectDetector.from_preset(
+    "hf://keras/retinanet_resnet50_fpn_coco"
+)
+
+input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
+object_detector(input_data)
+```
+
+## Fine-tune the pre-trained model
+```python3
+backbone = keras_hub.models.Backbone.from_preset(
+    "hf://keras/retinanet_resnet50_fpn_coco"
+)
+preprocessor = keras_hub.models.RetinaNetObjectDetectorPreprocessor.from_preset(
+    "hf://keras/retinanet_resnet50_fpn_coco"
+)
+model = RetinaNetObjectDetector(
+    backbone=backbone,
+    num_classes=len(CLASSES),
+    preprocessor=preprocessor
+)
+```
+
+## Custom training the model
+```python3
+image_converter = keras_hub.layers.RetinaNetImageConverter(
+    scale=1/255
+)
+
+preprocessor = keras_hub.models.RetinaNetObjectDetectorPreprocessor(
+    image_converter=image_converter
+)
+# Load a pre-trained ResNet50 model. 
+# This will serve as the base for extracting image features.
+image_encoder = keras_hub.models.Backbone.from_preset(
+    "resnet_50_imagenet" 
+)
+
+# Build the RetinaNet Feature Pyramid Network (FPN) on top of the ResNet50 
+# backbone. The FPN creates multi-scale feature maps for better object detection
+# at different sizes.
+backbone = keras_hub.models.RetinaNetBackbone(
+    image_encoder=image_encoder,
+    min_level=3,
+    max_level=5,
+    use_p5=False 
+)
+model = RetinaNetObjectDetector(
+    backbone=backbone,
+    num_classes=len(CLASSES),
+    preprocessor=preprocessor
+)
+```