Animal image identification and classification using deep neural networks techniques

Animal identification research, there haven't been many effective methods introduced, especially in the area of predator species. In this article, we provide a reliable learning strategy for categorising animals from camera-trap photos captured in naturally inhabited areas with high densities of people and noise. To deal with noisy labels, we offered two distinct network architectures—one with clean samples and the other without. We separate the training data into groups with various properties using k-means clustering. Then, other networks are trained using these groupings. Then, using maximum voting, these more diverse networks are used to jointly forecast or correct sample labels. We test the effectiveness of the suggested method using two publicly accessible camera-trap picture datasets, Snapshot Serengeti and Panama-Netherlands. Our findings show that our method is more accurate and surpasses state-of-the-art techniques for classifying animal species from camera-trap photos with high levels of label noise.