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PakhiChini: Automatic Bird Species Identification Using Deep Learning

机译:PakhiChini:使用深度学习自动识别鸟类

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The sector of entire image classification has recently found outstanding accomplishment in Convolutaional Neural Network. Lately, leveraging pretrained Convolutional Neural Networks (CNN) offer a much better illustration of an input image. ResNet [1] is one the top pretrained CNN networks that is mostly used in deep learning as pretrained CNN model. In this paper, we propose a deep learning model that is capable of identifying individual birds from an input image. We tend to additionally leverage pretrained ResNet model as pretrained CNN networks with base model to encode the images. Usually, birds are found in diverse scenarios which are seen in different sizes, shapes, sizes, colors from human point of view. Conducted experiments will be using the entity of different dimensions, cast and celerity to study recognition performance. We achieved a top-5 accuracy of 97.98% on our classifications.
机译:最近,在卷积神经网络中发现了整个图像分类领域的杰出成就。最近,利用预训练的卷积神经网络(CNN)提供了更好的输入图像说明。 ResNet [1]是顶级的预训练CNN网络之一,在预学习的CNN模型中主要用于深度学习。在本文中,我们提出了一种深度学习模型,该模型能够从输入图像中识别单个鸟类。我们倾向于将预训练的ResNet模型用作具有基本模型的预训练的CNN网络,以对图像进行编码。通常,人们从不同的角度看到鸟类,从人类的角度来看它们的大小,形状,大小,颜色都不同。进行的实验将使用不同维度,角色和角色的实体来研究识别性能。我们在分类中达到了57.9%的前五名准确性。

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