Image Based Bird Species Identification Using CNN

Project Code :TCMAPY672


The main objective of the project is to classify the species of birds using deep learning methods


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  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. Due to climate changes, many species of flora and fauna are endangered. In order to protect them, we must first identify the species to which it belongs and the special care which needs to be taken care of for their survival. More than 10,000 species are part of the ecosystem. Identifying the species of the bird from an image is a challenging task as it requires techniques such as image processing and Convolutional Neural Network (CNN). CNN is a very challenging Research Area with lots of issues as a slight variation in the image can be perceived as a completely new image. In our approach, we are using the transfer learning approach for training our neural model.

Keywords: —Deep Neural Network, Computer Vision, Convolutional Neural Network, Image Classification, Image Recognition, Transfer Learning, Machine learning, Bird species classification.



NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram



  • Operating system :  Windows 7 or 7+
  • RAM : 8 GB
  • Hard disc or SSD:  More than 500 GB  
  • Processor :  Intel 3rd generation or high or Ryzen with 8 GB Ram


  • Software’s : Python 3.6 or high version
  • IDE : PyCharm.
  • Framework :  Flask

Learning Outcomes

·         About Classification in machine learning.

·         About pre-processing techniques.

·         About CNN algorithm.

·         About Deep Learning Technique

·         Knowledge on PyCharm Editor.

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