What Is Overfitting in Object Detection?
Explore the concept of overfitting in YOLO object detection, its impact on model accuracy, and how to detect it using validation metrics. Understand practical methods such as early stopping, data augmentation, and adjusting loss gains to prevent overfitting and improve your model's ability to generalize to new data.
We'll cover the following...
Overfitting is a problem that can occur when training a machine learning model. It happens when the model learns the specific details of the training data too well, and as a result, it cannot generalize to new data. This can lead to poor performance on new data.
How does overfitting impact OD performance?
Note: Overfitting increases false positives (predictions of objects that do not exist in the image) in our dataset. It faces difficulty in predicting if there is an object in a given location.
Let’s imagine a scenario in which we have trained a YOLO model to detect people. After monitoring the validation losses, we realized that our model was overfitting. What would inference look like with our overfitted model?
One possible outcome can be that our model has learned extremely complex patterns in the dataset—patterns that might not even exist in reality. This suggests that the model has picked up noise from the training data. For instance, instead of accurately detecting persons, the model might be detecting traffic lights as a person. Such behavior might be an indication of overfitting, and that the model has become too specialized in recognizing the objects present in the training set. As a result, it fails to generalize well to new, unseen data.
How to detect overfitting
To detect overfitting in YOLO, we can use the following methods: