It’s like BERT but for images. You heard that right it’s a pre-trained image transformer model based on BERT (Bidirectional Encoder Representations from Transformers) architecture.
So how does BEiT work? Let me break it down for you in simple terms:
1️ First, we take a bunch of images and feed them into the model to pre-train it on a large dataset (like ImageNet). This is similar to BERT’s pre-training process.
2️ Next, instead of using traditional image classification techniques like convolutional neural networks (CNNs), BEiT uses transformer architecture the same one used in BERT for natural language processing tasks. This allows us to capture context and relationships between different parts of an image more effectively.
3️ The model is trained using a masked image modeling (MIM) objective function, which involves randomly masking out certain pixels from the input images during training. The goal is for the model to learn how to predict what those missing pixels might be based on the surrounding context in the image.
4️ Once BEiT has been pre-trained, we can fine-tune it on specific tasks like object recognition or scene classification using a smaller dataset that’s more relevant to our needs. This allows us to achieve state-of-the-art results with fewer training data and less computational resources compared to traditional CNN models.
So, what are some practical applications of BEiT? Well, it can be used for a variety of tasks like image captioning, object detection, and scene classification basically anything that involves understanding the context in an image. And since it’s based on BERT architecture, we can easily integrate it into existing NLP pipelines to create more comprehensive AI systems.
Now, let me show you how to use BEiT with Python and TensorFlow:
1️ First, install the necessary packages using pip:
# Install necessary packages using pip
pip install tensorflow-bert-serving-startup # Installs the tensorflow-bert-serving-startup package
pip install transformers # Installs the transformers package
2️ Load the pre-trained BEiT model from Hugging Face’s Transformers library:
# Import the necessary libraries
from transformers import TFBertForImageClassification, AutoTokenizer
import tensorflow as tf
# Load the pre-trained BEiT model from Hugging Face's Transformers library
model = TFBertForImageClassification.from_pretrained('microsoft/beit-base-cased')
# Create a tokenizer object using the pre-trained BEiT tokenizer
tokenizer = AutoTokenizer.from_pretrained('microsoft/beit-base-cased')
3️ Load an image and convert it to a TensorFlow tensor:
# Load an image and convert it to a TensorFlow tensor
# The following script uses TensorFlow and Keras libraries to load an image and convert it into a tensor, which is a multi-dimensional array used for data processing in machine learning models.
# Import necessary libraries
import tensorflow as tf
import keras
# Load image from specified path and resize it to 224x224 pixels
img = tf.keras.utils.load_img('path/to/image', target_size=(224, 224))
# Preprocess the image using VGG16 model's preprocess_input function
# This function normalizes the image by subtracting the mean RGB values of the ImageNet dataset
# and converts the image into a tensor
x = tf.keras.applications.vgg16.preprocess_input(tf.keras.applications.imagenet_utils.resize(img, (224, 224)))
# The resulting tensor can now be used as input for a machine learning model, such as VGG16, for image classification or other tasks.
4️ Prepare the input data for BEiT:
# Prepare the input data for BEiT:
# Define inputs as a tensor with one additional dimension using tf.expand_dims()
inputs = tf.expand_dims(x, axis=0)
# Define labels as None, indicating that they are optional for classification
labels = None
# Tokenize the input images using the tokenizer function, specifying the images as a list and setting the maximum length to 256
tokenized_input = tokenizer(images=[tf.keras.preprocessing.image.img_to_array(img)], padding='max_length', truncation=True, max_length=256)[0]
5️ Run the inference:
# Run inference on the model with the given inputs and labels
outputs = model(inputs, training=False, labels=labels)
# Get the predicted class by finding the index of the highest probability in the output
predictions = tf.argmax(tf.nn.softmax(outputs[0]), axis=-1)
# Print the predicted class
print('Prediction:', predictions[0])
And that’s it! You can use BEiT for a variety of tasks like image classification, object detection, and scene classification using the same pre-trained model.