Tag Archives | hyperparameters

Optimizing dlib shape predictor accuracy with find_min_global

In this tutorial you will learn how to use dlib’s find_min_global function to optimize the options and hyperparameters to dlib’s shape predictor, yielding a more accurate model. A few weeks ago I published a two-part series on using dlib to train custom shape predictors: Part one: Training a custom dlib shape predictor Part two: Tuning […]

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Tuning dlib shape predictor hyperparameters to balance speed, accuracy, and model size

In this tutorial, you will learn how to optimally tune dlib’s shape predictor hyperparameters and options to obtain a shape predictor that balances speed, accuracy, and model size. Today is part two in our two-part series on training custom shape predictors with dlib: Part #1: Training custom dlib shape predictors (last week’s tutorial) Part #2: Tuning […]

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How to tune hyperparameters with Python and scikit-learn

In last week’s post, I introduced the k-NN machine learning algorithm which we then applied to the task of image classification. Using the k-NN algorithm, we obtained 57.58% classification accuracy on the Kaggle Dogs vs. Cats dataset challenge: The question is: “Can we do better?” Of course we can! Obtaining higher accuracy for nearly any machine learning algorithm […]

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