mahdi-darvish/Comparative-Analysis-of-ConvNet-Architecture-on-Bird-Species-Dataset

An implementation of famous convolutional neural networks on Bird species dataset using Python 3, Keras, and Tensorflow.

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Experimental

This project offers a comparative analysis of different convolutional neural network (ConvNet) architectures for classifying bird species from images. It takes bird images as input and produces classifications and performance metrics for various ConvNet models. This is ideal for researchers or students in computer vision or ornithology looking to understand how different neural network designs impact bird species identification.

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Use this if you want to explore and compare the effectiveness of established ConvNet architectures for image classification tasks, specifically with bird species imagery.

Not ideal if you are looking for a ready-to-use application for immediate bird species identification without diving into the underlying model architectures and code.

bird-species-identification image-classification ornithology computer-vision-research deep-learning-analysis
No License Stale 6m No Package No Dependents
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Adoption 5 / 25
Maturity 8 / 25
Community 8 / 25

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Last pushed

Mar 17, 2021

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