Transfer Learning Techniques for Image Classification with TensorFlow
If you are working in the field of computer vision, you must have stumbled upon the task of image classification. Image classification is a vital problem in computer vision that aims to correctly assign a label to an image from a set of predefined categories. With the advent of the deep learning revolution, the performance of image classification has significantly increased, largely owing to the advancements in neural network techniques.
One of the most popular deep learning frameworks widely used for image classification is TensorFlow. TensorFlow offers multiple options to train deep learning models and one of them is the transfer learning technique.
What is Transfer Learning?
Transfer learning is a deep learning technique where a pre-trained model is used as a starting point for a new task instead of training a model from scratch. In transfer learning, the pre-trained model acts as a feature extractor which brings its potential to the new task. Transfer learning is effective when we have limited data and computational resources, as it allows the new model to leverage the knowledge gained from the pre-trained model.
Transfer Learning Techniques for Image Classification
1. Feature Extraction:
In feature extraction technique, we take a pre-trained model and remove the fully connected layers (the last few layers responsible for classification) and add our own fully connected layers. After adding the new layers, we retrain only these layers on the new dataset. This technique can be used when the new dataset is small and similar to the original dataset that the pre-trained model was trained on.
2. Fine-tuning:
In fine-tuning, instead of using the pre-trained model as a feature extractor only, we can also continue training the model on the new dataset by unfreezing the last few layers of the pre-trained neural network. The model is then trained end-to-end to classify the new dataset. This technique can be used when the new dataset is large and has a different distribution.
Advantages of Transfer Learning
Transfer learning has advantages such as:
- It improves the model’s performance as it allows leveraging the knowledge from the pre-trained model.
- It saves time and computational resources by reducing overall training time.
- It reduces overfitting as the pre-trained models are already trained on a diverse set of images.
Conclusion
In summary, transfer learning is an effective technique for image classification in computer vision, especially when the dataset is small or the computational resources are limited. TensorFlow provides several pre-trained models which can be used as a starting point for building and training new models for specific tasks. We hope this article sheds some light on transfer learning techniques with TensorFlow and encourages you to try them in your next image classification project.