CEEC: Mixed-Precision EfficientNetB3 Transfer Learning for Fine-Grained Endemic Bird Classification and Biodiversity Monitoring

Abstract—Identifying bird species is crucial for biodiversity
conservation, yet many of the 25 endemic Indian species are
visually similar and hard to distinguish. Fine-grained classification is challenging due to high intra-class variability in
pose, lighting, and background, coupled with subtle inter-class
differences in plumage and morphology. Despite growing use
of deep learning for avian identification, large-scale, balanced
datasets and tailored models for Indian endemic species remain
scarce. This paper presents a deep learning framework trained
on a curated dataset of 37,500 images spanning 25 endemic
Indian bird species. Our contributions include: (i) a balanced,
annotated dataset; (ii) a reproducible pipeline with systematic
data augmentation and mixed-precision training; and (iii) a
novel EfficientNetB3-based architecture (CEEC) optimized for
fine-grained classification. The CEEC model employs a frozen
EfficientNetB3 backbone pretrained on ImageNet, combined with
a custom classification head incorporating batch normalization,
dropout, and dense layers to reduce overfitting while preserving discriminative features. Among evaluated models, including
custom CNNs, ResNet152V2, and EfficientNet variants, CEEC
achieved superior performance: 98.74% test accuracy, 0.05487
test loss, and 0.9873 F1-score.