Update README.md with new model card content

README.md

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 library_name: keras-hub
 ---
 ### Model Overview
+EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.
 
+We develop EfficientNets based on AutoML and Compound Scaling. In particular, we first use AutoML MNAS Mobile framework to develop a mobile-size baseline network, named as EfficientNet-B0; Then, we use the compound scaling method to scale up this baseline to obtain EfficientNet-B1 to EfficientNet-B7.
 
+This class encapsulates the architectures for both EfficientNetV1 and EfficientNetV2. EfficientNetV2 uses Fused-MBConv Blocks and Neural Architecture Search (NAS) to make models sizes much smaller while still improving overall model quality.
+
+This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
+
+## Links
+
+* [EfficientNet Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/efficientnet-quickstart-kerashub)
+* [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)(ICML 2019)
+* [Based on the original keras.applications EfficientNet](https://github.com/keras-team/keras/blob/master/keras/applications/efficientnet.py)
+* [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298) (ICML 2021)
+* [EfficientNet API Documentation](coming soon)
+* [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. Full code examples for each are available below.
+
+| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
+|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| efficientnet_b0_ra_imagenet | 5.3M      | EfficientNet B0 model pre-trained on the ImageNet 1k dataset with RandAugment recipe. |
+| efficientnet_b0_ra4_e3600_r224_imagenet | 5.3M      | EfficientNet B0 model pre-trained on the ImageNet 1k dataset by Ross Wightman. Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from timm and 'ResNet Strikes Back'. |
+| efficientnet_b1_ft_imagenet | 7.8M      | EfficientNet B1 model fine-tuned on the ImageNet 1k dataset. |
+| efficientnet_b1_ra4_e3600_r240_imagenet | 7.8M      | EfficientNet B1 model pre-trained on the ImageNet 1k dataset by Ross Wightman. Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 small, mixed with go-to hparams from timm and 'ResNet Strikes Back'. |
+| efficientnet_b2_ra_imagenet | 9.1M      | EfficientNet B2 model pre-trained on the ImageNet 1k dataset with RandAugment recipe. |
+| efficientnet_b3_ra2_imagenet | 12.2M      | EfficientNet B3 model pre-trained on the ImageNet 1k dataset with RandAugment2 recipe. |
+| efficientnet_b4_ra2_imagenet | 19.3M      | EfficientNet B4 model pre-trained on the ImageNet 1k dataset with RandAugment2 recipe. |
+| efficientnet_b5_sw_imagenet | 30.4M      | EfficientNet B5 model pre-trained on the ImageNet 12k dataset by Ross Wightman. Based on Swin Transformer train / pretrain recipe with modifications (related to both DeiT and ConvNeXt recipes). |
+| efficientnet_b5_sw_ft_imagenet | 30.4M      | EfficientNet B5 model pre-trained on the ImageNet 12k dataset and fine-tuned on ImageNet-1k by Ross Wightman. Based on Swin Transformer train / pretrain recipe with modifications (related to both DeiT and ConvNeXt recipes). |
+| efficientnet_el_ra_imagenet | 10.6M      | EfficientNet-EdgeTPU Large model trained on the ImageNet 1k dataset with RandAugment recipe. |
+| efficientnet_em_ra2_imagenet | 6.9M      | EfficientNet-EdgeTPU Medium model trained on the ImageNet 1k dataset with RandAugment2 recipe. |
+| efficientnet_es_ra_imagenet | 5.4M      | EfficientNet-EdgeTPU Small model trained on the ImageNet 1k dataset with RandAugment recipe. |
+| efficientnet2_rw_m_agc_imagenet | 53.2M      | EfficientNet-v2 Medium model trained on the ImageNet 1k dataset with adaptive gradient clipping. |
+| efficientnet2_rw_s_ra2_imagenet | 23.9M      | EfficientNet-v2 Small model trained on the ImageNet 1k dataset with RandAugment2 recipe. |
+| efficientnet2_rw_t_ra2_imagenet | 13.6M      | EfficientNet-v2 Tiny model trained on the ImageNet 1k dataset with RandAugment2 recipe. |
+| efficientnet_lite0_ra_imagenet | 4.7M      | EfficientNet-Lite model fine-trained on the ImageNet 1k dataset with RandAugment recipe. |
+
+## Model card
+https://arxiv.org/abs/1905.11946
+
+## Example Usage
+Load
+```python
+classifier = keras_hub.models.EfficientNetImageClassifier.from_preset(
+    "efficientnet_b0_ra_imagenet",
+)
+```
+Predict
+```python
+batch_size = 1
+images = keras.random.normal(shape=(batch_size, 96, 96, 3))
+classifier.predict(images)
+```
+Train, specify `num_classes` to load randomly initialized classifier head.
+```python
+num_classes = 2
+labels = keras.random.randint(shape=(batch_size,), minval=0, maxval=num_classes)
+classifier = keras_hub.models.EfficientNetImageClassifier.from_preset(
+    "efficientnet_b0_ra_imagenet",
+    num_classes=num_classes,
+)
+classifier.preprocessor.image_size = (96, 96)
+classifier.fit(images, labels, epochs=3)
+```
+
+## Example Usage with Hugging Face URI
+
+Load
+```python
+classifier = keras_hub.models.EfficientNetImageClassifier.from_preset(
+    "efficientnet_b0_ra_imagenet",
+)
+```
+Predict
+```python
+batch_size = 1
+images = keras.random.normal(shape=(batch_size, 96, 96, 3))
+classifier.predict(images)
+```
+Train, specify `num_classes` to load randomly initialized classifier head.
+```python
+num_classes = 2
+labels = keras.random.randint(shape=(batch_size,), minval=0, maxval=num_classes)
+classifier = keras_hub.models.EfficientNetImageClassifier.from_preset(
+    "efficientnet_b0_ra_imagenet",
+    num_classes=num_classes,
+)
+classifier.preprocessor.image_size = (96, 96)
+classifier.fit(images, labels, epochs=3)
+```