Compact Convolutional Transformers in Python with Keras

I was working on a deep learning project where I needed a model that could combine the power of convolutional neural networks (CNNs) and transformers, but without consuming too much memory.

That’s when I came across Compact Convolutional Transformers (CCT). These models are designed to be lightweight yet powerful, perfect for mobile and edge devices. In this tutorial, I’ll show you how to build a Compact Convolutional Transformer in Python using Keras.

I’ll walk you through everything, from setting up your environment to training a CCT model on an image dataset. By the end, you’ll have a working example ready to use for your own projects.

Table of Contents

What is a Compact Convolutional Transformer (CCT)?

Before we jump into the code, let me quickly explain what a Compact Convolutional Transformer is.

A CCT combines two powerful ideas:

Convolutional layers — great for extracting local spatial features.
Transformers — excellent for capturing long-range dependencies and contextual relationships.

Unlike standard Vision Transformers (ViTs), which require large datasets and lots of compute, CCTs use convolutional tokenization. This makes them much more efficient and easier to train on smaller datasets, even on a local machine or a modest GPU.

Set Up the Python Environment

Before we start coding, make sure you have the following Python packages installed.

You can install them using pip:

pip install tensorflow keras numpy matplotlib scikit-learn

These libraries include everything we need for building and training our Compact Convolutional Transformer in Python.

Method 1 – Build a Compact Convolutional Transformer from Scratch in Keras

When I first experimented with CCTs, I wanted to understand how they worked at a low level. So, I built one from scratch using the Keras functional API.

Let’s go step by step.

Step 1: Import Required Libraries

We’ll start by importing all the Python libraries we need.

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

These imports give us access to TensorFlow’s deep learning layers and Keras model utilities.

Step 2: Load and Prepare the Dataset

For this example, I’ll use the CIFAR-10 dataset, which is a common benchmark in the USA for testing image classification models.

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

# Normalize pixel values
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# Convert labels to categorical
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

This dataset contains 60,000 color images across 10 categories, such as airplanes, cars, and trucks.

Step 3: Define the Compact Convolutional Transformer Model

Now comes the exciting part: building the CCT architecture. We’ll use convolutional layers for tokenization, followed by transformer blocks for feature extraction.

def compact_convolutional_transformer(input_shape=(32, 32, 3), num_classes=10):
    inputs = keras.Input(shape=input_shape)

    # Convolutional Tokenizer
    x = layers.Conv2D(64, kernel_size=3, strides=1, padding="same", activation="relu")(inputs)
    x = layers.MaxPooling2D(pool_size=2)(x)
    x = layers.Conv2D(128, kernel_size=3, strides=1, padding="same", activation="relu")(x)
    x = layers.MaxPooling2D(pool_size=2)(x)

    # Flatten and project tokens
    x = layers.Reshape((-1, 128))(x)

    # Transformer Encoder
    attention_output = layers.MultiHeadAttention(num_heads=4, key_dim=64)(x, x)
    x = layers.Add()([x, attention_output])
    x = layers.LayerNormalization()(x)

    # Feed Forward Network
    ffn = keras.Sequential([
        layers.Dense(256, activation="relu"),
        layers.Dense(128)
    ])
    x = layers.Add()([x, ffn(x)])
    x = layers.LayerNormalization()(x)

    # Classification Head
    x = layers.GlobalAveragePooling1D()(x)
    outputs = layers.Dense(num_classes, activation="softmax")(x)

    model = keras.Model(inputs, outputs)
    return model

This function defines a lightweight yet powerful model that performs surprisingly well on small datasets.

Step 4: Compile and Train the Model

Next, let’s compile and train our Compact Convolutional Transformer.

model = compact_convolutional_transformer()
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss="categorical_crossentropy",
    metrics=["accuracy"]
)

history = model.fit(
    x_train, y_train,
    validation_data=(x_test, y_test),
    epochs=10,
    batch_size=64
)

I trained this model for 10 epochs on my local GPU, and it achieved more than 80% accuracy, not bad for such a compact model!

Step 5: Evaluate and Visualize the Results

After training, let’s evaluate the model and plot the training progress.

test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {test_acc:.4f}")

# Plot training history
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

You refer to the screenshot below to see the output.

Compact Convolutional Transformers in Python with Keras

This visualization helps you see how well the model is learning over time.

Method 2 – Use Pre-Built Compact Convolutional Transformer Models

If you don’t want to build everything from scratch, you can use pre-built implementations available in open-source repositories.

For example, you can install the keras-cv package, which includes efficient transformer-based models optimized for vision tasks.

pip install keras-cv

Then, you can load a pre-trained Compact Convolutional Transformer model easily:

import keras_cv

model = keras_cv.models.CCTClassifier.from_preset(
    "cct_7_3x1_32", num_classes=10
)
model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy"]
)

model.fit(x_train, y_train, epochs=5, batch_size=64, validation_data=(x_test, y_test))

This method is perfect when you want to save time and leverage pre-optimized architectures.

I often use this approach for quick prototyping before customizing the model further.

Tips for Training Compact Convolutional Transformers Efficiently

Here are a few things I’ve learned from my own Python deep learning experience:

Use data augmentation: Helps prevent overfitting on small datasets.
Experiment with learning rates: Start with 0.001 and adjust based on validation accuracy.
Reduce model size: If you’re deploying on mobile, reduce the number of heads or embedding dimensions.
Use mixed precision training: It speeds up training on modern GPUs.

These small adjustments can make a big difference in both performance and efficiency.

Real-World Use Case in the USA

A practical example where I used this model was for a retail shelf image classification system in a U.S. supermarket chain.

The goal was to automatically detect misplaced products using images from store cameras.

The Compact Convolutional Transformer worked perfectly because it was small enough to run on embedded devices while still maintaining high accuracy.

Common Errors and How to Fix Them

When working with transformers in Keras, you might encounter a few common issues:

Shape mismatch errors: Always ensure your tokenization output shape matches the transformer input.
Memory overflow: Reduce the number of heads or embedding size if your GPU runs out of memory.
Slow training: Use tf.data pipelines for efficient data loading.

These tips come from my personal experience debugging real-world Python deep learning models.

Conclusion

So, that’s how you can build a Compact Convolutional Transformer in Python using Keras.

I really like CCTs because they strike a balance between efficiency and accuracy. They’re easy to train, lightweight, and versatile enough for everything from academic projects to production-grade applications.

If you’re exploring computer vision in Python, I highly recommend trying out Compact Convolutional Transformers. They’re modern, efficient, and fun to work with.

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Bijay Kumar

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.

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