Overfitting is one of the biggest challenges for developers regarding computer vision with machine learning. But what is overfitting, and how can you avoid it? Scroll through this article to discover more details about overfitting in machine learning and its connection with computer vision.
Understanding the Concept of Overfitting
At times, a machine learning model will only accurately predict outcomes for a specific set of training data. It will fail to make the right predictions for new data. This type of undesirable behavior from a machine learning model is called overfitting.
An overfitted machine learning model learns too many inaccurate values and noise from the training data. Therefore, it becomes incapable of predicting future observations. As a result, the precision and accuracy of the model no longer remain intact.
Reasons Behind Overfitting
A model using practical machine learning for computer vision will only deliver accurate predictions by generalizing all types of data inside the domain. However, overfitting occurs due to the inability to generalize, which makes the model fit too closely to the training data. A few reasons behind overfitting are as follows:
1. The training data size is too small and lacks enough samples to correctly represent the potential input data values.
2. The training data includes heaps of noisy data or irrelevant information.
3. The machine learning model trains from only one set of sample data for a long time.
4. The machine learning model is so complex that it learns the noise present within the training data.
Ways to Prevent Overfitting
When you can diversify or scale your training data, you will be able to prevent overfitting machine learning models. A few data science strategies to prevent overfitting are as follows:
Regularization
This method involves different training or optimization techniques to reduce overfitting. The regularization process eliminates factors that don’t influence the prediction outcomes by checking the importance of different features.
Early Stopping
If you pause the training before the model learns the noise within the dataset, you can stop overfitting. But finding the right time to pause is crucial. If you end up pausing too fast, your model won’t deliver accurate results.
Data Augmentation
The machine learning technique changes the sample data before it gets processed by a model. It is possible by modifying the input data in subtle ways like applying flipping, translation, and rotation to images. By performing data augmentation in moderation, you will be able to make your dataset seem unique to the model every time.
Ensembling
This process involves combining predictions from different machine learning algorithms. Bagging and boosting are two primary ensemble methods. Boosting involves training machine learning models one after the other. Meanwhile, bagging involves training models in parallel.
Summing up
Preventing overfitting in computer vision applications is crucial for making them perform accurately against unseen datasets. Different methods are available to prevent a statistical model from fitting exactly against its training data.
FAQs:
Q.1 What is Unity computer vision?
Unity engine combines different computer vision technologies and libraries to develop different applications. From AR and VR applications to educational tools, Unity and computer vision technologies are combined for various purposes.
Q.2 What is an under-fitted model in computer vision?
An under-fitted model in computer vision is extremely simple, with minimal features and insufficient data for building an effective model. These models have a low variance and high bias.
Q.3 What technique is useful for avoiding overfitting in computer vision?
The regularization technique in machine learning is ideal for avoiding overfitting in computer vision. By making the coefficient transition towards zero, this process can help reduce errors.
