Update README.md with new model card content

README.md

@@ -7,5 +7,99 @@ tags:
 pipeline_tag: image-classification
 ---
 ### Model Overview
+# Model Summary
 
+Instantiates the ResNet architecture amended by “bag of tricks” modifications.
+## Reference
+[Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/abs/1812.01187)
 
+ResNetVd introduces two key modifications to the standard ResNet. First, the initial convolutional layer is replaced by a series of three successive convolutional layers. Second, shortcut connections use an additional pooling operation rather than performing downsampling within the convolutional layers themselves.
+## Links
+
+* [ResNetVD Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/resnetvd-quickstart-notebook)
+* [ResNet and ResNetVD series Doc](https://paddleclas.readthedocs.io/en/latest/models/ResNet_and_vd_en.html)
+* [ResNetVD Model Card](https://arxiv.org/abs/1812.01187)
+* [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: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Full code examples for each are available below.
+
+
+| Preset name            | Parameters | Description                                                                                     |
+|------------------------|------------|-------------------------------------------------------------------------------------------------|
+| `resnet_vd_18_imagenet`   | 11.72M     | 18-layer ResNetVD model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.|
+| `resnet_vd_34_imagenet`  | 21.84M     | 34-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
+| `resnet_vd_50_imagenet` | 25.63M     | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.  |
+| `resnet_vd_50_ssld_imagenet`   | 25.63M    | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation.  |
+| `resnet_vd_50_ssld_v2_imagenet`           | 25.63M    | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation and AutoAugment.  |
+| `resnet_vd_50_ssld_v2_fix_imagenet`           | 25.63M   | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.                                              |
+| `resnet_vd_101_imagenet`        | 44.67M    | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
+| `resnet_vd_101_ssld_imagenet`  | 44.67M    | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation. |
+| `resnet_vd_152_imagenet`          | 60.36M    | 152-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
+| `resnet_vd_200_imagenet`          | 74.93M    | 200-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
+
+## Example Usage
+```python
+   
+from keras_hub.models import ResNetBackbone
+import keras
+import numpy as np
+
+input_data = np.ones(shape=(8, 224, 224, 3))
+
+# Pretrained backbone
+model = ResNetBackbone.from_preset("resnet_vd_50_ssld_v2_imagenet")
+output = model(input_data)
+
+# Randomly initialized backbone with a custom config
+model = ResNetBackbone(
+        input_conv_filters=[32, 32, 64],
+        input_conv_kernel_sizes=[3, 3, 3],
+        stackwise_num_filters=[64, 128, 256, 512],
+        stackwise_num_blocks=[3, 4, 5, 6],
+        stackwise_num_strides=[1, 2, 2, 2],
+        block_type="bottleneck_block_vd",
+    )
+output = model(input_data)
+
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+   
+from keras_hub.models import ResNetBackbone
+import keras
+import numpy as np
+
+input_data = np.ones(shape=(8, 224, 224, 3))
+
+# Pretrained backbone
+model = ResNetBackbone.from_preset("hf://keras/resnet_vd_50_ssld_v2_imagenet")
+output = model(input_data)
+
+# Randomly initialized backbone with a custom config
+model = ResNetBackbone(
+        input_conv_filters=[32, 32, 64],
+        input_conv_kernel_sizes=[3, 3, 3],
+        stackwise_num_filters=[64, 128, 256, 512],
+        stackwise_num_blocks=[3, 4, 5, 6],
+        stackwise_num_strides=[1, 2, 2, 2],
+        block_type="bottleneck_block_vd",
+    )
+output = model(input_data)
+
+```