https://github.com/macabdul9/flowers102-recognition

https://github.com/macabdul9/flowers102-recognition

competitive-data-science convolutional-neural-networks deep-learning deep-learning-algorithms ensemble-classifier flower-classification neural-networks transfer-learning

Last synced: 7 months ago
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• Host: GitHub
• URL: https://github.com/macabdul9/flowers102-recognition
• Owner: macabdul9
• License: mit
• Created: 2019-08-09T09:59:15.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-08-21T13:15:32.000Z (almost 7 years ago)
• Last Synced: 2025-04-05T20:42:19.614Z (about 1 year ago)
• Topics: competitive-data-science, convolutional-neural-networks, deep-learning, deep-learning-algorithms, ensemble-classifier, flower-classification, neural-networks, transfer-learning
• Language: Jupyter Notebook
• Size: 155 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# flowers102-recognition

### Tools, and Libraries used

- Python

- Google Colab GPU

- Deep Learning

- Convolutional Neural Network

- Transfer Learning

- Data Augmentation 

- EfficientNets (state-of-the-art)

- #### Libraries 

- numpy, pandas, matplotlib, keras, etc.

- ![#1589F0](https://placehold.it/15/1589F0/000000?text=+) `validation loss`

- ![#f03c15](https://placehold.it/15/f03c15/000000?text=+) `training loss`

![loss](logs/loss.svg)

- ![#1589F0](https://placehold.it/15/1589F0/000000?text=+) `validation accuracy`

- ![#f03c15](https://placehold.it/15/f03c15/000000?text=+) `training accuracy`

![acc](logs/acc.svg)

- combined graph