Finetuning Vision Transformers (VIT) with Huggingface Transformers and Tensorflow 2

With the increasing popularity of Vision Transformers (VIT), it has become important for developers to be able to finetune pre-trained VIT models for specific tasks. Huggingface Transformers and Tensorflow 2 provide a powerful combination for achieving this.

Huggingface Transformers

Huggingface Transformers is a popular open-source library that provides a wide variety of pre-trained models for natural language processing (NLP) and computer vision tasks. It also offers tools and utilities for finetuning these models on custom datasets.

Tensorflow 2

Tensorflow 2 is a powerful deep learning framework that allows for easy construction and training of neural networks. It supports a wide range of models and provides tools for efficient data processing and model evaluation.

Finetuning Vision Transformers with Huggingface Transformers and Tensorflow 2

Finetuning a pre-trained VIT model using Huggingface Transformers and Tensorflow 2 can be broken down into several key steps:

1. Load the pre-trained VIT model using Huggingface Transformers
2. Prepare the custom dataset for training and evaluation
3. Construct the finetuning pipeline using Tensorflow 2
4. Train the model on the custom dataset

Example Code

Here’s an example of how one might finetune a pre-trained VIT model using Huggingface Transformers and Tensorflow 2:

import tensorflow as tf
from transformers import ViTFeatureExtractor, TFELECTRAForImageClassification
from transformers import TFAutoModelForImageClassification

# Load the pre-trained VIT model and feature extractor
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = TFELECTRAForImageClassification.from_pretrained('google/vit-base-patch16-224')

# Prepare the custom dataset for training and evaluation
# This involves data preprocessing, splitting into training and validation sets, etc.

# Construct the finetuning pipeline using Tensorflow 2
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
train_step = tf.Variable(0, trainable=False)
schedule = tf.optimizers.schedules.PolynomialDecay(
  initial_learning_rate=1e-4,
  decay_steps=1000,
  end_learning_rate=1e-5
)
checkpoint = tf.train.Checkpoint(
  step=train_step, optimizer=optimizer, model=model
)

# Train the model on the custom dataset
for epoch in range(num_epochs):
  for images, labels in train_dataset:
    with tf.GradientTape() as tape:
      logits = model(images, training=True)
      loss = loss_fn(labels, logits)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    train_step.assign_add(1)
    if int(train_step) % 100 == 0:
      checkpoint.save(ckpt_prefix)

# Evaluate the finetuned model on the validation set
# This involves calculating metrics like accuracy, precision, and recall

Conclusion

Finetuning Vision Transformers with Huggingface Transformers and Tensorflow 2 is a powerful and flexible approach for adapting pre-trained models to specific tasks. By leveraging the capabilities of both Huggingface Transformers and Tensorflow 2, developers can achieve state-of-the-art performance on a wide range of computer vision tasks.