ner_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O B-Software I-Software I-Software I-Software I-Software I-Software B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software B-Citation I-Citation O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O
tokens: next section, we describe the framework developed and its features, followed by a description of the various methods implemented within it to tackle this problem. After this, we describe the data set provided for the challenge, and what additional techniques were utilized to ‘expand’ on this data set. We then go on to describe what metrics are presented by our framework, and how methods are evaluated, and compare the performances achieved with those achieved in other works. We conclude the work by describing further improvements that can be implemented in order to make gravitational lensing detection methods more efficient and more accurate. 1.1 LEXACTUM The framework developed in this work has been named Lens EXtrActor CaTania University of Malta (LEXACTUM). The first of its main features are the image augmentation techniques described in Section 2.2 which can be toggled on or off to train for a greater number of epochs without overfitting. Another feature is the modularity of the code, allowing for the rather easy development of new models, with very easy integration of new models into the pipeline. Other features include the ability to set parameters from the command line. Examples of such parameters are the data set path, whether to train a model or load one from disk, the name of the model (to train or load), the number of epochs to train for, the batch size, and whether to use image augmentation during training or not. LEXACTUM also uses a custom ‘Data Generator’, which loads and pre-processes images in batches with the CPU, while the GPU can train on the last batch of images. Apart from image augmentation during training, all images are normalized using ZScale (NOAO 1997 ). Like other components, the normalization method can be easily swapped out for other techniques. Furthermore, LEXACTUM comes with a ‘results’ package, which scores the trained models and calculates several metrics, described in detail in Section 3 . Moreover, LEXACTUM saves trained models to disk, and also provides functionality for loading trained models. Finally, the ‘visualize features’ component was created, which allows for the viewing of the feature maps at every convolutional layer that a trained model is ‘looking at’ during execution. All of the architectures described in Section 1.2.5 were implemented in LEXACTUM. All of these models were then trained from scratch, on both the Space and Ground data sets, using ZScale normalization and image augmentation, for a varying number of epochs. As a starting point, all models were trained for 5 or 10, 25 or 50, 100, and 250 epochs. After that, if, say, a particular model obtained promising results, and did not seem to be overfitting (judging by the loss and accuracy of the validation set) after 250 epochs, it would then be further trained for 500, or even 1000 epochs. 1.2 Literature review 1.2.1 Conventional methods The methods described in this subsection are not implemented in LEXACTUM, and are only presented to give