Kootstrap is a bootstrap to Keras. It is a technique of compile and loading a datasets into a Keras application by means of a few initial instructions that enable the introduction of the rest of the program from an input dataset.

Commands

Create a dataset with two classes.

cd maker/
python Main.py --mode dataset --dataset_name graffiti --classes graffiti,street

Create a subset of this dataset with 90% original images

python Main.py --mode compiler  --dataset_name graffiti --subset_name graffiti_per90_porp_default --per_images 90

Crawls images to each class from Flickr. This seed the dataset and compile the subsets.

cd ../crawler/
python Main.py --mode crawler,dataset --dataset dataset_example --classes graffiti,street --flickr_tags graffiti,street\ art;street --num_images 100

Execute a train with finetuning in Imagenet Model

cd ../trainer/
python Main.py --model_name model_example_1 --load_data dataset_example

Test the predictions on model with set of test compileted in graffiti_per90_porp_default

cd ../tester/
python Main.py --model_name model_example_1 --load_data graffiti_per90_porp_default

Compile a 1-Top with a histogram from test results:

cd ../analyzer/
python Main.py --model_name top --test_name testing_imagenet_test_set

Migrate datasets

if you have a dataset and want migrate try:

cd ../tools/
python Main.py --mode migrate --path_origin <PATH_FOLDER_WITH_CLASSES> --path_destiny <PATH_TO_KOOTSTRAP_FOLDER> 

if you want create a subset or recovery the metadata.json try:

cd ../tools/
python Main.py --mode fix --path_origin <PATH_TO_SUBSET_OR_DATASET>

Copy or move files to inside a dataset? Try:

python Main.py --mode transfer --path_origin <PATH_FOLDER_WITH_CLASSES> --path_destiny <PATH_TO_KOOTSTRAP_FOLDER>