Flower Species Recognition System

https://github.com/7ayush/FlowerRecognition

Summary of the project

• Pretrained state-of-the-art neural networks are used on University of Oxford's FLOWERS17 and FLOWERS102 dataset.
• Models used - 2 layers of convolution and 1 layer of fully connected network.
• Weights used - ImageNet
• Classifier used - Adam optimiser

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 MacOS 11 for development purposes and is fully functional on Windows and Linux as well

Licence

MIT License

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 : Adam Optimiser

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 : Adam Optimiser

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%