We share the pre-trained 512-VGG CNN model as research support in Mexico native flora identification. We believe that this pre-trained model may be a good starting point for future research in flower identification. Transfer learning from pre-trained models on ImageNet 2012 dataset was used.
Pre-trained model can be found here: https://github.com/jacluas/Mexico120FlowerModel/releases
The model is shared in the form of H5 format, that is, a file format to store structured data to easily share. Keras saves models in this format as it can easily store the weights and model configuration in a single file.
We used Keras version 2.2.4, with Tensorflow 1.13.1 as backend, and Python version 3.7.3.
You can evaluate a sample image by performing the following:
python predict.py MODEL_NAME.h5 IMAGE_TEST_PATCH TOP-KExamples Top-1:
python predict.py model/model_512_vgg TEST/Achillea_millefolium/AM1.jpeg
Predictions:
'Achillea millefolium', 0.9999955892562866 python predict.py mode/model_512_vgg TEST/Cordia_boissieri/CB1.jpeg -k 1
Predictions:
'Cordia boissieri', 1.0Examples Top-5:
python predict.py model/model_512_vgg TEST/Achillea_millefolium/AM1.jpeg -k 5
Predictions:
'Achillea millefolium', 0.9999955892562866
'Heliotropium angiospermum', 4.443768830242334e-06
'Turnera diffusa', 1.945797342695066e-11
'Asclepias subulata', 1.160856393650489e-11
'Verbesina encelioides', 9.17614335210759e-12 python predict.py model/model_512_vgg TEST/Cordia_boissieri/CB1.jpeg -k 5
Predictions:
'Cordia boissieri', 1.0
'Pontederia crassipes', 6.602185909088121e-09
'Argemone mexicana', 7.986839661855427e-11
'Oenothera speciosa', 4.6942175840891665e-11
'Cosmos bipinnatus', 3.5377999358168766e-13