Birds have an enormous impact on the ecosystem and various information about an area can be produced from the bird species prevalent in it. Ornithologists have been monitoring many bird species in order to gain a better understanding of their habitats. Image classification is a popular technique that can be used in order to classify different species of birds. An image classification approach for bird species identification has been proposed in this work. The approach is based on transfer learning using the pre-trained CNN architecture VGG16. The model is trained on more than 45000 images of 300 different species of birds, many of which are endangered. The model is able to achieve 96.44% accuracy on the training dataset while achieving a maximum accuracy of 96.8% on the test dataset.

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