Abstract: Wildlife detection and identification are critical components in biodiversity conservation and ecological monitoring. This study evaluates the effectiveness of two deep learning architectures ResNet-101 and Inception v3 in classifying and detecting wildlife species under diverse environmental conditions. Both models were trained on a curated wildlife dataset with standardized preprocessing techniques, including resizing images to a maximum of 800 pixels, RGB format.....
Key Word: Wildlife Object Detection, Deep Learning, Convolutional Neural Networks (CNNs), ResNet-101, Inception v3.
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