【Machine Learning】【Python】Chapter 4: Hard Negative Mining for Optimizing Training of SVM Models ---- "SVM Object Classification and Localization Detection"

Latest code GitHub link: https://github.com/HansRen1024/SVM-classification-localization


By optimizing parameters through PSO, the accuracy of the trained model has improved by about 4%. However, this is still not satisfactory. Therefore, we will try to optimize the training of the SVM model through hard negative mining. The basic principle is to first train a model using the original training set, and then predict all negative samples using sliding windows. If the prediction result for a window is positive, the window will be added to the training set with a specified label of negative. It is important to note that in the original data, the number of negative samples should be much larger than the number of positive samples.

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