newbie here in deep learning. My question is: I have an already trained object detection (yolov5) model for 3 classes [0,1,2]. Now, my next step is to classify one class , e.g. class [0] as anomaly or not. In other words, I need an additional classifier to further classify it into to sub-classes , i.e., anomalous or non-anomalous through the use of classifier or anomaly detection model. Can you give me an advice on how can I proceed with this? I will use GANs as anomaly detection model. This would be a great help. Thank you in advance.
1 Answers
one of the ways to solve your problems is through One-Class Learning (OCL). In OCL, the algorithm learns from only one user-defined interest class (in your case, non-anomalous objects of class 0) and classifies a new example as belonging to this interest class or not. Thus, you can adapt OCL algorithms to your problem. One of the ways is to use labeled examples of class 0 non-anomalous and use these examples in learning the algorithm. Finally, your algorithm will answer whether new instances of class 0 are non-anomalous (class of interest) or anomalous (non-interest class). Examples of OCL algorithms can be found at: https://scikit-learn.org/stable/modules/outlier_detection.html. It is noteworthy that these are traditional OCL algorithms. On the other hand, there are already versions of OCL algorithms based on deep learning.
In addition, I'll provide you with an example of anomaly detection using the One-Class Support Vector Machine (OCSVM), one of the most traditional famous OCL algorithms.
from sklearn.svm import OneClassSVM as OCSVM
normal_data_of_class_0 = [[],[],[],[], ......, []] #class 0 normal examples
ocsvm = OCSVM()
ocsvm.fit(normal_data_of_class_0)
data_of_class_0 = [[],[],[],[], ......, []] #class 0 new examples
y_pred = ocsvm.predict(data_of_class_0) # +1 == normal data (interest class) || -1 == abnormal data (outliers)
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