An End-to-End Deep Learning Framework for Animal Classification with Ensemble CNNs and Web Deployment

Animal-image classification is often evaluated as an
isolated model task, while the software path required to turn
a trained model into a usable system receives less attention.
This paper presents an applied deep-learning and softwareintegration workflow for closed-set classification of 30 animal
categories from a custom project corpus of 3,000 images.
The workflow combines image standardization, duplicate
screening before partitioning, a nominal class-wise 70/15/15
split, training-only augmentation, transfer learning, fixed classindex alignment, heterogeneous CNN fusion, and a browseraccessible Flask inference service. The deployed ensemble combines MobileNetV3Small, EfficientNetV2B0, and DenseNet121
through equal-weight averaging of aligned softmax outputs.
On a 448-image development validation set, the observed
development result was 98.44% accuracy with macro precision,
recall, and F1 of 99%, 98%, and 98%, respectively.