feat: add the process of using the pre-trained model

refering to https://github.com/huggingface/huggingface_hub/issues/595

README.md

@@ -20,3 +20,53 @@ The main ideas are:
 
 - Shifted Patch Tokenization
 - Locality Self Attention
+
+# Use the pre-trained model
+
+The model is pre-trained on the CIFAR100 dataset with the following hyperparameters:
+```python
+# DATA
+NUM_CLASSES = 100
+INPUT_SHAPE = (32, 32, 3)
+BUFFER_SIZE = 512
+BATCH_SIZE = 256
+
+# AUGMENTATION
+IMAGE_SIZE = 72
+PATCH_SIZE = 6
+NUM_PATCHES = (IMAGE_SIZE // PATCH_SIZE) ** 2
+
+# OPTIMIZER
+LEARNING_RATE = 0.001
+WEIGHT_DECAY = 0.0001
+
+# TRAINING
+EPOCHS = 50
+
+# ARCHITECTURE
+LAYER_NORM_EPS = 1e-6
+TRANSFORMER_LAYERS = 8
+PROJECTION_DIM = 64
+NUM_HEADS = 4
+TRANSFORMER_UNITS = [
+    PROJECTION_DIM * 2,
+    PROJECTION_DIM,
+]
+MLP_HEAD_UNITS = [
+    2048,
+    1024
+]
+```
+I have used the `AdamW` optimizer with cosine decay learning schedule. You can find the entire implementation in the keras blog post.
+
+To use the pretrained model:
+```python
+loaded_model = from_pretrained_keras("keras-io/vit-small-ds")
+_, accuracy, top_5_accuracy = loaded_model.evaluate(test_ds)
+print(f"Test accuracy: {round(accuracy * 100, 2)}%")
+print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%")
+```
+
+For an indepth understanding of the model uploading and downloading process one can refer to this [colab notebook](https://colab.research.google.com/drive/1nCMhefqySzG2p8wyXhmeAX5urddQXt49?usp=sharing).
+
+Important: The data augmentation pipeline is excluded from the model. TensorFlow `2.7` has a weird issue of serializaiton with augmentation pipeline. You can follow [this GitHub issue](https://github.com/huggingface/huggingface_hub/issues/593) for more updates. To send images through the model, one needs to make use of the `tf.data` and `map` API to map the augmentation.