Understand what a Pretrained model and Feature extraction are with real-life examples—for beginners.

What is a Pretrained Model?

A pretrained model is a machine learning model that has already been trained on a large dataset to recognize patterns. Instead of starting from scratch and training a model from zero, you take this model that already “knows” a lot about certain types of data (like images or text) and use it for your task. It saves time because it has learned general patterns that are useful in many situations.

Photo by Marius Masalar on Unsplash

Think of it like this:

• Imagine you have an experienced chef who already knows how to cook lots of different dishes. If you ask them to cook something new (like a new recipe), they already have the skills and knowledge to make it faster and better than someone who is learning to cook for the first time.

Example of a Pretrained Model in Image Recognition:

• A model trained on millions of labeled images from ImageNet can already recognize general features in images, like shapes, edges, and objects like “cats,” “dogs,” or “cars.” If you wanted to use this model for something specific (e.g., recognizing a new category of images), you don’t need to train it from scratch — you use the knowledge it already has and fine-tune it for your task.

What is Feature Extraction?

Feature extraction is like taking the knowledge the pretrained model has learned (patterns, shapes, and characteristics) and using it to create a set of “useful pieces of information” from the data. These useful pieces (or features) summarize the important parts of the data, which can then be used to classify or recognize new data.

Here’s an easy way to understand:

• Feature extraction is like taking a photo of a person and noting important characteristics: “This person has black hair, glasses, and is wearing a blue shirt.” You’ve summarized the important parts of the photo in a few words. That’s what feature extraction does — it reduces the data into key characteristics that help to classify or understand it.

Example of Feature Extraction:

Imagine you’re tasked with classifying images of animals (cats and dogs). You don’t want to analyze the entire image pixel by pixel — it’s too much data and too complex. Instead, you extract features like:

• “Is the animal furry?”
• “Does it have pointy ears?”
• “What shape is its nose?”

By extracting these features, you reduce the amount of data and focus only on the key characteristics that matter for recognizing the animal.

How Pretrained Models and Feature Extraction Work Together

When you use a pretrained model, you are often using it to extract features from data (like images). The model has already been trained to recognize important patterns in images, so you use its knowledge to pull out useful features from new images.

Here’s how the process works:

1. Load Pretrained Model: You load a model that has already been trained on a huge dataset (like ImageNet).
2. Feed New Data to the Model: You give it a new image that the model has never seen before (e.g., a picture of a new type of animal).
3. Extract Features from the Model: Instead of asking the model to classify the image directly, you stop the process at one of the final layers of the model. This gives you a set of features the model has extracted from the image (like edges, textures, shapes).
4. Use the Features: Now you use these extracted features to train a simple classifier (like Logistic Regression or SVM) on your specific task. This classifier can then use the features to make predictions (e.g., is this a cat or a dog?).

Example:

If you’re using a pretrained image model to classify animals, you might extract features like the shape of the ears, texture of the fur, etc., and then use those features to classify whether the animal is a cat, dog, or something else.

Real-Life Scenario Example:

Let’s say you are building a system to recognize flowers from images. Training a model from scratch would be very difficult and take a lot of time. Instead:

• You use a pretrained model that has already been trained on thousands of images of various objects, including flowers.
• Instead of making the model predict the type of flower directly, you use it to extract features from the images of flowers (like the petal shape, color patterns, etc.).
• Then, you use these features to train a smaller classifier (e.g., Logistic Regression) that will tell you what type of flower it is.

Why Use Pretrained Models and Feature Extraction?

1. Saves Time: The pretrained model has already done most of the learning for you. You don’t need to start from scratch.
2. Better Accuracy: Pretrained models have been trained on massive amounts of data, so they are usually more accurate than models trained on small datasets.
3. Simplifies Your Task: Instead of training complex deep learning models, you just extract features and focus on building a simpler classifier for your specific task.