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Identification of objects from an image using machine learning is an emerging research topic. It is known to the scientific community that the labelling of objects by human intelligence in the current scenario of high volume data consumes a large amount of time and produces an error of 5% as observed in the case of ImageNet. In this regard, machine learning methods have become more popular and effective as they produce better results. However, the performance of different machine learning methods vary while identifying objects from images. To address this fact, an object labelling problem has been formulated by considering the images that contain objects of 10 different classes either in the form of solid, hollow and mixed. In order to label the objects in such three types of images, two different approaches are followed. First, a training dataset is created of size 240 samples where 24 samples are present in each …