Update README.md with new model card content

README.md

@@ -1,32 +1,98 @@
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 library_name: keras-hub
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-This is a [`CSPNet` model](https://keras.io/api/keras_hub/models/csp_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:** csp_net_backbone
-* **trainable:** True
-* **stem_filters:** 64
-* **stem_kernel_size:** 7
-* **stem_strides:** 4
-* **stackwise_depth:** [3, 3, 5, 2]
-* **stackwise_strides:** [1, 2, 2, 2]
-* **stackwise_num_filters:** [256, 512, 1024, 2048]
-* **stage_type:** csp
-* **block_type:** bottleneck_block
-* **output_strides:** 32
-* **groups:** 32
-* **activation:** leaky_relu
-* **bottle_ratio:** 1.0
-* **block_ratio:** 0.5
-* **expand_ratio:** 1.0
-* **stem_padding:** valid
-* **stem_pooling:** max
-* **avg_down:** False
-* **down_growth:** False
-* **cross_linear:** True
-* **image_shape:** [None, None, 3]
-* **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
+This class represents the CSPDarkNet architecture.
+
+**Reference**
+
+-  [CSPNet Paper](https://arxiv.org/abs/1911.11929)
+
+For transfer learning use cases, make sure to read the
+[guide to transfer learning & fine-tuning](https://keras.io/guides/transfer_learning/).
+
+## Links
+* [CSPNet Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/cspnet-quickstart-kerashub)
+* [CSPDarkNet API Documentation](https://keras.io/keras_hub/api/models/cspnet/)
+* [CSPDarkNet Model Card](https://huggingface.co/timm/cspdarknet53.ra_in1k)
+* [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:
+
+```
+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   |
+|-----------------------|------------|---------------|
+| `csp_darknet_53_ra_imagenet` | 27642184 | A CSP-DarkNet (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.|
+| `csp_resnext_50_ra_imagenet` | 20569896 | A CSP-ResNeXt (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.|
+| `csp_resnet_50_ra_imagenet` | 21616168 | A CSP-ResNet (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.|
+| `darknet_53_imagenet` | 41609928 | A DarkNet image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.|
+
+## Example Usage
+```python
+input_data = np.ones(shape=(8, 224, 224, 3))
+
+# Pretrained backbone
+model = keras_hub.models.CSPNetBackbone.from_preset("csp_resnext_50_ra_imagenet")
+model(input_data)
+
+# Randomly initialized backbone with a custom config
+    model = keras_hub.models.CSPNetBackbone(
+        stem_filters=32,
+        stem_kernel_size=3,
+        stem_strides=1,
+        stackwise_depth=[1, 2, 4],
+        stackwise_strides=[1, 2, 2],
+        stackwise_num_filters=[32, 64, 128],
+        block_type="dark",
+)
+
+model(input_data)
+
+#Use cspnet for image classification task
+model = keras_hub.models.ImageClassifier.from_preset("csp_resnext_50_ra_imagenet")
+
+#Use Timm presets directly from HuggingFace
+model = keras_hub.models.ImageClassifier.from_preset('hf://timm/cspdarknet53.ra_in1k')
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+input_data = np.ones(shape=(8, 224, 224, 3))
+
+# Pretrained backbone
+model = keras_hub.models.CSPNetBackbone.from_preset("hf://keras/csp_resnext_50_ra_imagenet")
+model(input_data)
+
+# Randomly initialized backbone with a custom config
+    model = keras_hub.models.CSPNetBackbone(
+        stem_filters=32,
+        stem_kernel_size=3,
+        stem_strides=1,
+        stackwise_depth=[1, 2, 4],
+        stackwise_strides=[1, 2, 2],
+        stackwise_num_filters=[32, 64, 128],
+        block_type="dark",
+)
+
+model(input_data)
+
+#Use cspnet for image classification task
+model = keras_hub.models.ImageClassifier.from_preset("hf://keras/csp_resnext_50_ra_imagenet")
+
+#Use Timm presets directly from HuggingFace
+model = keras_hub.models.ImageClassifier.from_preset('hf://timm/cspdarknet53.ra_in1k')
+```