Classificação de Aves Predadoras: Fine-tuning Progressivo em Redes Neurais Convolucionais

This paper presents a process for classifying predatory birds byfamily and species. The motivation for this study arises from thehigh variability observed among birds of different species and theimportance of performing classification efficiently and in a timelymanner. Additionally, this work aims to analyze the impact of usingRGB channels in comparison to grayscale images on classificationperformance, as well as the effect of applying data augmentationtechniques during training. The dataset contains 42,475 images,distributed across 6 families and 41 species. The process employsfine-tuning, using the ResNet-50 model. Early stopping was appliedto control overfitting and obtain the best model. The test resultshighlight the effectiveness of the proposed process in classificationtasks, with performance varying across different input configurations.For species classification, the model trained with grayscalechannels achieved an F1-Score of 0.80. Using RGB channels improvedthe performance significantly, resulting in an F1-Score of0,86. Further applying data augmentation techniques to the RGBslightly improved the metrics, achieving an F1-Score of 0.87. Theseresults demonstrate the benefits of incorporating color informationand data augmentation in enhancing classification accuracy.