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In this competition, we’re challenged to build a machine learning model that identifies the type of flowers in a dataset of images (for simplicity, we’re sticking to just over 100 types).

Home Page: https://www.kaggle.com/c/flower-classification-with-tpus

Jupyter Notebook 100.00%
tpu flower-classification transfer-learning tensorflow kaggle-competition multiclass-classification machine-learning efficientnet-keras ensemble-model

flower-classification's Introduction

Flower-classification

Course Project for CS 795 - Practical Machine Learning | Yaohang Li

Requirements to run the program

  • python 3.7 with anaconda
  • numpy
  • pandas
  • scikitlearn
  • pickle
  • random
  • seaborn
  • matplotlib
  • TensorFlow
  • Keras
  • efficientNet

Note

  • All the above mentioned libraries can be downloaded from anaconda packages.
  • for efficientNet, we need to follow steps mentioned in the EfficientNet lib .

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