![]() ![]() ![]() 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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