Update README.md with new model card content

README.md

@@ -1,18 +1,95 @@
 ---
 library_name: keras-hub
 ---
-This is a [`MiT` model](https://keras.io/api/keras_hub/models/mi_t) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-Model config:
-* **name:** mi_t_backbone
-* **trainable:** True
-* **depths:** [2, 2, 2, 2]
-* **hidden_dims:** [64, 128, 320, 512]
-* **image_shape:** [224, 224, 3]
-* **num_layers:** 4
-* **blockwise_num_heads:** [1, 2, 5, 8]
-* **blockwise_sr_ratios:** [8, 4, 2, 1]
-* **max_drop_path_rate:** 0.1
-* **patch_sizes:** [7, 3, 3, 3]
-* **strides:** [4, 2, 2, 2]
-
-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 MixTransformer architecture to be used as a backbone for the SegFormer architecture. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
+
+References:
+- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) # noqa: E501
+- [Based on the TensorFlow implementation from DeepVision](https://github.com/DavidLandup0/deepvision/tree/main/deepvision/models/classification/mix_transformer) # noqa: E501
+
+## Links
+* [MiT Quickstart Notebook: coming soon]()
+* [MiT API Documentation: coming soon]()
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-Hub
+pip install -U -q keras>=3
+```
+
+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://dl.fbaipublicfiles.com/segment_anything/. Full code examples for each are available below.
+Here's the table formatted similarly to the given pattern:
+
+Here's the updated table with the input resolutions included in the descriptions:
+
+| Preset name              | Parameters | Description                                                                                      |
+|--------------------------|------------|--------------------------------------------------------------------------------------------------|
+| mit_b0_ade20k_512        | 3.32M      | MiT (MixTransformer) model with 8 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. |
+| mit_b1_ade20k_512        | 13.16M     | MiT (MixTransformer) model with 8 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. |
+| mit_b2_ade20k_512        | 24.20M     | MiT (MixTransformer) model with 16 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. |
+| mit_b3_ade20k_512        | 44.08M     | MiT (MixTransformer) model with 28 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. |
+| mit_b4_ade20k_512        | 60.85M     | MiT (MixTransformer) model with 41 transformer blocks, trained on the ADE20K dataset with an input resolution of 512x512 pixels. |
+| mit_b5_ade20k_640        | 81.45M     | MiT (MixTransformer) model with 52 transformer blocks, trained on the ADE20K dataset with an input resolution of 640x640 pixels. |
+| mit_b0_cityscapes_1024   | 3.32M      | MiT (MixTransformer) model with 8 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+| mit_b1_cityscapes_1024   | 13.16M     | MiT (MixTransformer) model with 8 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+| mit_b2_cityscapes_1024   | 24.20M     | MiT (MixTransformer) model with 16 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+| mit_b3_cityscapes_1024   | 44.08M     | MiT (MixTransformer) model with 28 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+| mit_b4_cityscapes_1024   | 60.85M     | MiT (MixTransformer) model with 41 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+| mit_b5_cityscapes_1024   | 81.45M     | MiT (MixTransformer) model with 52 transformer blocks, trained on the Cityscapes dataset with an input resolution of 1024x1024 pixels. |
+
+### Example Usage
+Using the class with a `backbone`:
+
+```
+import tensorflow as tf
+import keras_cv
+import numpy as np
+
+images = np.ones(shape=(1, 96, 96, 3))
+labels = np.zeros(shape=(1, 96, 96, 1))
+backbone = keras_cv.models.MiTBackbone.from_preset("mit_b1_cityscapes_1024")
+
+# Evaluate model
+model(images)
+
+# Train model
+model.compile(
+     optimizer="adam",
+     loss=keras.losses.BinaryCrossentropy(from_logits=False),
+     metrics=["accuracy"],
+)
+model.fit(images, labels, epochs=3)
+```
+
+## Example Usage with Hugging Face URI
+
+Using the class with a `backbone`:
+
+```
+import tensorflow as tf
+import keras_cv
+import numpy as np
+
+images = np.ones(shape=(1, 96, 96, 3))
+labels = np.zeros(shape=(1, 96, 96, 1))
+backbone = keras_cv.models.MiTBackbone.from_preset("hf://keras/mit_b1_cityscapes_1024")
+
+# Evaluate model
+model(images)
+
+# Train model
+model.compile(
+     optimizer="adam",
+     loss=keras.losses.BinaryCrossentropy(from_logits=False),
+     metrics=["accuracy"],
+)
+model.fit(images, labels, epochs=3)
+```