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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