A pre-trained model is a machine learning (ML) model that has been trained on a large - scale dataset. It can be further fine - tuned for specific tasks, serving as a valuable starting point in ML model development. These models come with a set of pre-determined weights and biases, which can be adjusted according to the requirements of a particular task.
The utilization of pre-trained models offers numerous benefits. It allows leveraging the knowledge and experience accumulated from extensive training, saves both time and resources in the model-building process, and enhances the overall performance of the model. Pre-trained models are typically trained on vast and diverse datasets, enabling them to recognize a broad spectrum of patterns and features. This provides a solid basis for fine-tuning, leading to a substantial improvement in the model's performance.
Pre-trained models exist in various forms. For example, there are language models, object detection models, and image classification models. Convolutional neural networks (CNNs) are commonly employed as the backbone for image classification models. These models are trained to classify images into predefined categories.
For object recognition models, which are designed to identify and classify objects in images or videos, CNNs or region-based convolutional neural networks (R-CNNs) are often used as the fundamental architecture. In the case of language models, which are trained to predict the next word in a sequence, recurrent neural networks (RNNs) or transformers are frequently utilized as the underlying framework.
In summary, pre-trained models are an essential tool in the field of machine learning. They can be effectively used as a starting point for ML model development, with their initial weights and biases adaptable for specific tasks, thereby significantly enhancing model performance.