Introduction to Pretrained Models Beginner

Pretrained models are AI models that have already been trained on large datasets and can be reused for new tasks. Instead of training a model from scratch (which requires massive data and compute), you can leverage existing models and adapt them to your specific needs.

What Are Pretrained Models?

A pretrained model is a neural network that has been trained on a large dataset to learn general features and patterns. These learned representations can then be applied to new, related tasks — a technique called transfer learning.

Transfer Learning

Transfer learning is the practice of taking a model trained on one task and reusing it (with or without modification) for a different task:

1. Feature Extraction

   Use the pretrained model as a fixed feature extractor. Feed your data through the model and use its outputs as inputs to a simpler classifier. No retraining of the pretrained model is needed.

2. Fine-tuning

   Start with the pretrained weights and continue training on your specific dataset. The model adapts its learned features to your task while retaining general knowledge.

3. Zero-shot / Few-shot

   Modern large models can perform tasks they were never explicitly trained on, using natural language instructions or just a few examples.

Why Use Pretrained Models?

Types of Pretrained Models

Where to Find Pretrained Models

Model Formats