A pre-trained model is a machine learning model which has already been trained on a large dataset, often on a general task. It can be fine-tuned for a specific task, saving computational resources and time.


Imagine you want to become a chef. Instead of learning all the cooking techniques from scratch, you find a book from a reputed chef addressing various cooking techniques. So you have a great starting point. You learn more special dishes with time. Similarly, a pre-trained model is like that book. It’s a model already trained on lots of data. We use this model as a starting point and teach it more specific things.

In-depth explanation

In machine learning, training models from scratch can be costly both in terms of computational power and time. Pre-trained models serve to mitigate these concerns. They are machine learning models which have already undergone training on a large benchmark dataset, typically from a broad domain of knowledge.

Pre-training can be seen as a form of transfer learning. After pre-training on a large data set, the model can then be tuned, or “fine-tuned”, on a smaller, more specific dataset. This fine-tuning process is essentially the continuation of the initial training phase, where the model continues to learn but this time it’s more specialized to the problem at hand.

For example, in the field of natural language processing (NLP), a model might be pre-trained on a large corpus of text (like all Wikipedia articles). It learns to understand lots of things about language from this data—with this general understanding, the model is then fine-tuned to perform particular tasks, such as sentiment analysis on restaurant reviews. As another example, in image processing, a model might be pre-trained on a large dataset of images (like ImageNet), learning to detect features like edges and shapes, before being fine-tuned for more specific tasks like distinguishing between cat and dog images.

One of the main benefits of pre-trained models is that they allow us to train models even when we don’t have a large amount of labeled data for our specific task. They also reduce the computational cost and time required for model training.

Transfer Learning, Fine-Tuning, Training From Scratch, Supervised Learning, Unsupervised Learning, Convolutional Neural Network (CNN), Deep Learning.