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These days, it's hard to classify and forecast which bird species will be around since some of these species are so scarce. Birds in diverse settings naturally seem different to humans in terms of size, shape, color, and angle. Beyond that, visual cues are more useful than auditory cues for identifying bird species. The fact that people can identify the birds in the photos is also easier to grasp. Therefore, the Caltech-UCSD Birds 200 [CUB-200-2011] dataset is used for both training and testing purposes by this technique. To create an autograph using tensor flow, a picture is gray scaled and then fed into a deep convolutional neural network (DCNN) algorithm, which generates many comparison nodes. The testing dataset is used to compare these various nodes, and a score sheet is then generated. After looking at the score sheet, it may use the highest score to predict which bird species are needed.

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Bird Genus Classification and Identification Using Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • P. Prashanth Kumar,
  • V. Supraja,
  • K. Pranathi,
  • E. Naveena

摘要

These days, it's hard to classify and forecast which bird species will be around since some of these species are so scarce. Birds in diverse settings naturally seem different to humans in terms of size, shape, color, and angle. Beyond that, visual cues are more useful than auditory cues for identifying bird species. The fact that people can identify the birds in the photos is also easier to grasp. Therefore, the Caltech-UCSD Birds 200 [CUB-200-2011] dataset is used for both training and testing purposes by this technique. To create an autograph using tensor flow, a picture is gray scaled and then fed into a deep convolutional neural network (DCNN) algorithm, which generates many comparison nodes. The testing dataset is used to compare these various nodes, and a score sheet is then generated. After looking at the score sheet, it may use the highest score to predict which bird species are needed.