Image classification using deep learning

In the framework of this project where trained two famous NN's - Inceptionv3 and EfficientNetb1. Project include following sections

• Explanatory Data Analasys

• Training pipeline

Data

• Flowers Recognition Dataset with 5 classes of flowers

• Total images: 4317 images

• Daisy: 764 images

• Dandelion: 1052 images

• Rose: 784 images

• Sunflower: 733 images

• Tulip: 984 images

Result

• EfficientNet show pretty good results with:

• Train Accuracy: 94%

• Validation Accuracy: 82%

• Precision/recall: 91%, 86%

• Matthews Corr. coef.: 0.86

• Cohen Kappa: 0.86

Used libraries and frameworks

During project where used following libraries:

• PyTorch Lightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

• Pandas - awesome data processing library

• torchvision - Tools for working with PyTorch CV related problems.

• Wandb - Experiment Tracker

• torchmetrics - PyTorch metrics

• sklearn - Machine Learning library

Installation

Project requires Pytorch 1.11.0.

Install the dependencies via environment.yml