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Conservation efforts for endangered bird species necessitate accurate species identification. This paper introduces an enhanced classification model utilizing GoogLeNet architecture to improve the identification accuracy of endangered bird species.

Dataset Collection and Preprocessing:

A comprehensive dataset containing images of various endangered bird species was compiled and meticulously preprocessed. The dataset’s diversity and quality were vital in training and validating the proposed classification model.

Enhancement of Random Classifier:

The conventional random classifier underwent substantial improvements by integrating GoogLeNet, a deep convolutional neural network renowned for its accuracy in image classification tasks. Applied fine-tuning and optimization techniques to specifically adapt GoogLeNet to suit the unique characteristics of endangered bird species.

Training and Evaluation:

The enhanced classifier was trained using the curated dataset, employing transfer learning and augmentation strategies to boost performance. Rigorous evaluation through cross-validation ensured robustness and reliability of the model. Evaluation metrics included accuracy, precision, recall, and F1 score.

Results and Discussion:

Experimental results showcased a significant improvement in species identification accuracy compared to conventional random classifiers. The GoogLeNet-based model demonstrated superior performance in distinguishing between endangered bird species, even in cases of subtle visual differences.

Conclusion and Future Prospects:

The utilization of GoogLeNet in refining the random classifier proved highly effective in enhancing endangered bird species identification. The promising results highlight the potential for broader applications in conservation biology and wildlife monitoring. Future research could explore deploying this model in real-world conservation efforts and adapting it for other endangered species domains.

This abstract provides a succinct overview of a research paper focusing on enhancing bird species classification accuracy using GoogLeNet within the context of endangered species conservation.

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