- 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
- Theano or TensorFlow
sudo pip install theano
orsudo pip install tensorflow
- Keras
sudo pip install keras
- NumPy
sudo pip install numpy
- matplotlib
sudo pip install matplotlib
and you also need to do thissudo apt-get install python-dev
- seaborn
sudo pip install seaborn
- h5py
sudo pip install h5py
- scikit-learn
sudo pip install scikit-learn
- This project used MacOS 11 for development purposes and is fully functional on Windows and Linux as well
MIT License
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% |