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Repository Details

🌺🌻 Using state-of-the-art pre-trained Deep Neural Net architectures for Flower Species Recognition

Flower Species Recognition System

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This repo contains the code for conference paper titled Flower Species Recognition System using Convolutional Neural Networks and Transfer Learning, by I.Gogul and V.Sathiesh Kumar, Proceedings of ICSCN-2017 conference, IEEE Xplore Digital Library.

Summary of the project

  • Pretrained state-of-the-art neural networks are used on University of Oxford's FLOWERS17 and FLOWERS102 dataset.
  • Models used - Xception, Inception-v3, OverFeat, ResNet50, VGG16, VGG19.
  • Weights used - ImageNet
  • Classifier used - Logistic Regression
  • Tutorial for this work is available at - Using Pre-trained Deep Learning models for your own dataset

Update (16/12/2017): Included two new deep neural net models namely InceptionResNetv2 and MobileNet.

Dependencies

  • Theano or TensorFlow sudo pip install theano or sudo pip install tensorflow
  • Keras sudo pip install keras
  • NumPy sudo pip install numpy
  • matplotlib sudo pip install matplotlib and you also need to do this sudo apt-get install python-dev
  • seaborn sudo pip install seaborn
  • h5py sudo pip install h5py
  • scikit-learn sudo pip install scikit-learn

System requirements

  • This project used Windows 10 for development purposes and Odroid-XU4 for testing purposes.

Licence

MIT License

Usage

  • Organize dataset - python organize_flowers17.py
  • Feature extraction using CNN - python extract_features.py
  • Train model using Logistic Regression - python train.py

Show me the numbers

The below tables shows the accuracies obtained for every Deep Neural Net model used to extract features from FLOWERS17 dataset using different parameter settings.

  • Result-1

    • test_size : 0.10
    • classifier : Logistic Regression
Model Rank-1 accuracy Rank-5 accuracy
Xception 97.06% 99.26%
Inception-v3 96.32% 99.26%
VGG16 85.29% 98.53%
VGG19 88.24% 99.26%
ResNet50 56.62% 90.44%
MobileNet 98.53% 100.00%
Inception
ResNetV2
91.91% 98.53%
  • Result-2

    • test_size : 0.30
    • classifier : Logistic Regression
Model Rank-1 accuracy Rank-5 accuracy
Xception 93.38% 99.75%
Inception-v3 96.81% 99.51%
VGG16 88.24% 99.02%
VGG19 88.73% 98.77%
ResNet50 59.80% 86.52%
MobileNet 96.32% 99.75%
Inception
ResNetV2
88.48% 99.51%

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