This is our final year project as 3rd Year BCA students, we built a model based on the concepts of transfer learning by using Efficient Net as our pretrained model, trained it on our data and evaluated it using metrics such as F1 Score, Precision, Recall and Accuracy as well as building a confusion Matrix. The following libraries were used in this project:
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Numpy : NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
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Pandas : pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language
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MatplotLib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations.
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Scikit-Learn : Simple and efficient tools for predictive data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable - BSD license
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TensorFlow.Keras : Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch. Keras is: Simple – but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter. Flexible – Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned. Powerful – Keras provides industry-strength performance and scalability: it is used by organizations including NASA, YouTube, or Waymo.
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PIL.Image : The Image module provides a class with the same name which is used to represent a PIL image. The module also provides a number of factory functions, including functions to load images from files, and to create new images.