Python3 is required alogn with cudnna and CUDA Toolkit.
Use the package manager pip to install the required dependencies.
pip install -r requirements.txtDownload the dataset from https://www.robots.ox.ac.uk/~vgg/data/flowers/102/
Create a folder called data and move the downloaded archive. Then execute
matlab -nodisplay -nosplash -nodesktop -r "run('split_dataset_paper.m');exit;"in the same folder.
trainer.py [-h] [--batch [BATCH]] [--arch [ARCHITECTURE]] [--opt [OPTIMIZER]] [--clr [CLR]] [--step [STEP]] [--dropout [DROPOUT]] [--config [CONFIG]] [--mp] [--da] [--epoch [EPOCH]]
optional arguments:
-h, --help show this help message and exit
--batch [BATCH] Batch size used during training
--arch [{efficientnetb4,frozenefficientnetb4,inceptionv3,resnet18}]
Architecture
--opt [{Adam,SGD}] Optimizer
--clr [{triangular,triangular2,exp}]
Cyclical learning rate
--step [STEP] Step size
--dropout [DROPOUT] Dropout rate (when used with FrozenEfficientNetB4 it's used for the freeze rate)
--config [CONFIG] Configuration file
--mp Enable mixed precision operations (16bit-32bit)
--da Enable Data Augmentation
--epoch [EPOCH] Set the number of epochs
The script will produce plots and checkpoints in ./output/plots and ./output/checkpoints
python learningratefinder.py [-h] [--batch [BATCH]] [--arch [ARCHITECTURE]] [--opt [OPTIMIZER]] [--dropout [DROPOUT]] [--config [CONFIG]] [--da] [--freeze [FREEZE]] [--epoch [EPOCH]]optional arguments:
-h, --help show this help message and exit
--batch [BATCH] Batch size used during training
--arch [{efficientnetb4,frozenefficientnetb4,inceptionv3,resnet18}]
Architecture
--opt [{Adam,SGD}] Optimizer
--dropout [DROPOUT] Dropout rate
--config [CONFIG] Configuration file
--da Enable Data Augmentation
--freeze [FREEZE] Frozen layers
--epoch [EPOCH] Set the number of epochs
To visualize Saliency Map and Grad-CAM run
python3 visualize.pyThe image must be store with the following pattern ./images/{class}/{image}.jpg
The repository is hosted in Github https://github.com/firaja/aml
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