Background: Recently, animal image classification has involved the impracticalityof deep learning models due to high computational demands, limiting their use inwildlife monitoring. This calls for the crucial necessity of lightweight models for real-time animal identification in resource-limited environments like wildlife cameras andmobile devices. Achieving accurate animal classification in these settings becomes acrucial concern for advancing classification methods.

Objectives: The goal of this research is to evaluate lightweight transfer learningmodels for classifying animal images while balancing computational efficiency andaccuracy. The objectives include analyzing the models’ performance and providingmodel selection criteria based on performance and efficiency for resource-constraintenvironments. This study contributes to the advancement of machine learning inwildlife preservation and environmental monitoring, which is critical for accuratespecies identification.

Methods: The proposed methodology involves conducting a thorough literaturereview to identify lightweight transfer learning models for image classification. TheAnimal-90 dataset was utilized, comprising images of ninety distinct animal species.To train the dataset, selected pre-trained models, MobileNetV2, EfficientNetB3,ShuffleNet, SqueezeNet, and MnasNet were employed with custom classificationheads. A 5-fold Cross-Validation technique was used to validate the model. Acombined metric approach is applied to rank the models based on the results fromthe metrics, Accuracy, Inference time, and number of parameters.

Results: The experimental outcomes revealed EfficientNetB3 to be the most ac-curate but also at the same time it has the highest number of parameters amongother models. Friedman’s test has rejected the null hypothesis of models havingsimilar performance. The combined metric approach ranked ShuffletNet as the topmodel among the compared models in terms of performance and efficiency.

Conclusions: The research unveiled the commendable performance of all the mod-els in animal image classification, with ShuffleNet achieving the top rank among allother models in terms of accuracy and efficiency. These lightweight models, espe-cially ShuffleNet, show promise in managing limited resources while ensuring accurateanimal classification and confirming their reliability in wildlife conservation