Bird Species Identification Using Deep Learning

PROJECT ABSTRACT:

Bird identification is a difficult undertaking that frequently results in ambiguous labelling. Even skilled bird watchers dispute on the species of a bird when presented with an image of one. It’s a demanding task that tests both people and computers’ visual talents.

Although different bird species have the same core set of elements, their shape and appearance can change drastically. Due to differences in lighting and background, as well as great diversity in stance, intraclass variance is high (e.g., flying birds, swimming birds, and perched birds that are partially occluded by branches).

Human knowledge of a species is insufficient to reliably identify a bird species, as it necessitates a great deal of skill in the subject of ornithology. Our study intends to use machine learning to assist amateur bird watchers in identifying bird species from photographs.

This research provides a deep neural network-based automated model for automatically identifying the species of a bird supplied as the test data set. For 400 different bird species, the model was trained and evaluated. Experimental analysis on dataset shows that our proposed Xception architecture model achieved an accuracy of bird identification with Training Accuracy of 99% and Validation Accuracy of 97%.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Xception Architecture.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

Python Projects

Bird Species Identification Using Deep Learning