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Now I will try to have a more detailed explanation: I have a dataset composed by images, images of trees (trees-folder), images of roses (roses-folder) and images of a house (house-folder)

The test dataset is composed by 1,2,3,4,5 folders. I know from my research that images in the five folders belong to those object classes, but they are disordered (images and are not labeled with the feature they represent, they are labeled with numbers,. For example I do not know how many trees, roses or houses are in folder2. This step is very important, because

  1. the images in the folders are grouped according a specific geographic location and
  2. the task of identifying features in an image was originally empirically defined by an expert (a human being) for each of the test folders. In other words I want to be SimpleCV be my expert and therefore (partly) automating this classification process.

Just a suggestion, as you explained in your answer, has it sense to label each folder in test dataset in the same way as the trained dataset, regardless of the content of the folder?