![]() The danger in the training process is that your model may overfit to the training set. The model formulates a prediction function based on the loss function, mapping the pixels in the image to an output. The smaller the value of the loss function, the better the model. A loss function is a way of describing the "badness" of a model. In order to guide your model to convergence, your model uses a loss function to inform the model how close or far away it is from making the correct prediction. When training a computer vision model, you show your model example images to learn from. Try Roboflow for Free What is Overfitting in Computer Vision? ![]() We're the easiest way to train and deploy computer vision object detection and classification models. ![]() Let Roboflow handle managing your train/test splits, dataset versioning, and more. ![]()
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