Xception Model for Accurate Indian Rock Python Detection in Deep Learning

The growing contacts between people and Indian rock reptiles, especially Russell's Viper and Python, have made precise and early identification more important to guarantee public safety and support of conservation initiatives. Using a dataset of 10,000 images gathered from the Kaggle platform, this work investigates the deployment of a fine-tuned Xception model to classify these species from images. Dataset collecting, preprocessing, model creation, and evaluation are part of the suggested approach. With a total of 21,394,730 parameters, the fine-tuned Xception model architecture, using depth-wise separable convolutions, showcases notable efficacy in feature extraction and classification tasks. Stable loss and accuracy charts show the model achieves a peak accuracy of 88% with minimum overfitting. Though Python's recall of 0.75 points areas for development, analysis of the classification report shows balanced performance. The findings support the model's potential as a strong tool for early identification, so supporting efforts at human safety, animal management, and resource allocation.