Flower Species Recognition System

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%