As a Python Keras developer with over four years of experience, I’ve seen how Transformer models revolutionized natural language processing and are now reshaping computer vision tasks. Among these, the Swin Transformer stands out for its ability to efficiently capture both local and global image features, making it a powerful tool for image classification.
In this article, I’ll walk you through how to build an image classification model using Swin Transformers in Keras. I’ll share practical methods and provide full code examples, so you can follow along and apply this in your own projects.
What is a Swin Transformer?
The Swin Transformer is a hierarchical vision Transformer that computes representations with shifted windows. Unlike traditional Transformers that process the entire image at once, Swin Transformers divide images into windows and shift these windows between layers. This approach reduces computational cost while maintaining strong performance.
If you’ve worked with convolutional neural networks (CNNs) before, you’ll appreciate how Swin Transformers can capture long-range dependencies more effectively.
Set Up the Environment for Swin Transformer in Keras
Before diving into the code, ensure you have the latest TensorFlow and Keras installed, as well as the tensorflow_addons package, which contains some useful layers for Transformer models.
pip install tensorflow tensorflow-addonsThis setup ensures compatibility with the custom layers used in Swin Transformer implementations.
Method 1: Use a Prebuilt Swin Transformer Model for Image Classification
The easiest way to get started is by using a prebuilt Swin Transformer model available in Keras or TensorFlow Hub. This method is great if you want quick results or want to fine-tune a pretrained model on your dataset.
Step 1: Load the Pretrained Swin Transformer
import tensorflow as tf
from tensorflow.keras import layers, models
# Load a pretrained Swin Transformer backbone (example: from tensorflow_hub or custom repo)
swin_backbone = tf.keras.applications.SwinTransformer(
include_top=False,
input_shape=(224, 224, 3),
weights='imagenet'
)Note: As of now, TensorFlow official applications do not include Swin Transformer by default, so you might need to load from a custom source or third-party repository.
Step 2: Add Classification Head
inputs = tf.keras.Input(shape=(224, 224, 3))
x = swin_backbone(inputs)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(10, activation='softmax')(x) # For 10 classes
model = models.Model(inputs, outputs)
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
model.summary()Step 3: Train the Model
# Assume train_ds and val_ds are your training and validation datasets
model.fit(train_ds, validation_data=val_ds, epochs=10)I executed the above example code and added the screenshot below.
This method leverages pretrained weights and requires minimal setup. It’s ideal for those who want to fine-tune on datasets like CIFAR-10 or custom image sets.
Method 2: Build Swin Transformer from Scratch in Keras
If you want more control or to understand the architecture deeply, you can build the Swin Transformer from scratch using Keras layers.
Step 1: Define Window-based Multi-Head Self-Attention
This is the core of Swin Transformer, performing self-attention within local windows.
import tensorflow_addons as tfa
def window_attention(x, window_size, num_heads):
# Partition input into windows and apply multi-head attention
# This function is simplified for illustration
# Actual implementation requires careful window partitioning and shifting
attn_layer = tfa.layers.MultiHeadAttention(num_heads=num_heads, key_dim=x.shape[-1])
return attn_layer(x, x)Step 2: Implement the Shifted Window Mechanism
Shift windows by half the window size to enable cross-window connections.
def shifted_window_partition(x, shift_size):
# Shift the feature map by shift_size pixels
# Then partition into windows
# This step is critical for Swin Transformer's performance
return tf.roll(x, shift=[-shift_size, -shift_size], axis=[1, 2])Step 3: Build the Swin Transformer Block
Combine layer normalization, window attention, MLP layers, and residual connections.
def swin_transformer_block(x, window_size, num_heads, mlp_dim):
shortcut = x
x = layers.LayerNormalization()(x)
x = window_attention(x, window_size, num_heads)
x = layers.Add()([x, shortcut])
shortcut = x
x = layers.LayerNormalization()(x)
x = layers.Dense(mlp_dim, activation='gelu')(x)
x = layers.Dense(x.shape[-1])(x)
x = layers.Add()([x, shortcut])
return xStep 4: Assemble the Full Model
Stack multiple Swin Transformer blocks with patch embedding and patch merging layers.
def build_swin_transformer(input_shape=(224,224,3), num_classes=10):
inputs = layers.Input(shape=input_shape)
x = layers.Conv2D(96, 4, strides=4)(inputs) # Patch embedding
x = layers.Reshape((-1, 96))(x) # Flatten patches
# Example: 2 Swin Transformer blocks
x = swin_transformer_block(x, window_size=7, num_heads=3, mlp_dim=192)
x = swin_transformer_block(x, window_size=7, num_heads=3, mlp_dim=192)
x = layers.GlobalAveragePooling1D()(x)
outputs = layers.Dense(num_classes, activation='softmax')(x)
model = models.Model(inputs, outputs)
return model
model = build_swin_transformer()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()I executed the above example code and added the screenshot below.
Training this model requires a well-prepared dataset and possibly GPU acceleration.
Method 3: Fine-Tuning Swin Transformer on a Custom Dataset
Fine-tuning a pretrained Swin Transformer on your own image dataset can yield great results with fewer epochs.
Step 1: Prepare the Dataset
Use tf.data to load and preprocess images.
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
'path_to_train',
image_size=(224, 224),
batch_size=32
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
'path_to_val',
image_size=(224, 224),
batch_size=32
)Step 2: Freeze Backbone and Train Classifier Head
swin_backbone.trainable = False
inputs = tf.keras.Input(shape=(224, 224, 3))
x = swin_backbone(inputs)
x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(num_classes, activation='softmax')(x)
model = models.Model(inputs, outputs)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_ds, validation_data=val_ds, epochs=5)Step 3: Unfreeze and Fine-Tune Entire Model
After initial training, unfreeze the backbone and fine-tune with a lower learning rate.
swin_backbone.trainable = True
model.compile(optimizer=tf.keras.optimizers.Adam(1e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_ds, validation_data=val_ds, epochs=10)This approach balances training speed and accuracy.
Working with Swin Transformers in Keras opens new frontiers for image classification. Whether you use pretrained models or build from scratch, the hierarchical and window-based design provides a powerful alternative to traditional CNNs.
I hope this guide helps you get started with Swin Transformers. Experiment with different datasets and hyperparameters to find what works best for your projects. Happy coding!
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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.
