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The aim of this project is to compare the effectiveness of different neural network architectures and pre-trained models for the classification of forest flora and fauna. Experiments were conducted on a dataset containing images of selected plants and animals found in the forest. The project covers the basics of convolutional neural networks, and compares the architectures of the networks used in the study. The results of the experiments compare the effectiveness of different convolutional neural network architect...