Multi-Class Image Classification Deep Learning Model for Kaggle 275 Bird Species Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Kaggle 275 Bird Species dataset is a multi-class classification situation where we attempt to predict one of several (for this dataset 275) possible outcomes.

INTRODUCTION: This dataset contains 275 bird species with 39364 training images, 1375 test images (5 per species), and 1375 validation images (5 per species. All images have a resolution of 224 X 224 X 3 color images in the jpg format. Each dataset includes 275 subdirectories, one for each bird species.

In this Take1 iteration, we will construct a simple three-layer CNN model to predict the species for the image.

ANALYSIS: The performance of the baseline model achieved an accuracy score of 68.87% after 50 epochs using the validation dataset. The same model processed the test dataset with an accuracy score of 69.24%.

CONCLUSION: In this iteration, the TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Kaggle 275 Bird Species dataset

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.