https://github.com/apurva-modi/flower-classification

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).
https://github.com/apurva-modi/flower-classification

efficientnet-keras ensemble-model flower-classification kaggle-competition machine-learning multiclass-classification tensorflow tpu transfer-learning

Last synced: 26 days ago
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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).

• Host: GitHub
• URL: https://github.com/apurva-modi/flower-classification
• Owner: apurva-modi
• Created: 2020-10-05T02:46:37.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2020-10-05T02:53:47.000Z (over 4 years ago)
• Last Synced: 2023-07-24T00:25:19.509Z (over 1 year ago)
• Topics: efficientnet-keras, ensemble-model, flower-classification, kaggle-competition, machine-learning, multiclass-classification, tensorflow, tpu, transfer-learning
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/c/flower-classification-with-tpus
• Size: 13.6 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://pypi.org/project/efficientnet/) .