Training

Convolutional Neural Network (CNN)

To train a new CNN, use the command-line interface (CLI) application bob tb train, available on your prompt. To use this CLI, you must define the input dataset that will be used to train the CNN, as well as the type of model that will be trained. You may issue bob tb train --help for a help message containing more detailed instructions.

Tip

We strongly advice training with a GPU (using --device="cuda:0"). Depending on the available GPU memory you might have to adjust your batch size (--batch).

Examples

To train Pasa CNN on the Montgomery dataset:

$ bob tb train -vv pasa montgomery --batch-size=4 --epochs=150

To train DensenetRS CNN on the NIH CXR14 dataset:

$ bob tb train -vv nih_cxr14 densenet_rs --batch-size=8 --epochs=10

Logistic regressor or shallow network

To train a logistic regressor or a shallow network, use the command-line interface (CLI) application bob tb train, available on your prompt. To use this CLI, you must define the input dataset that will be used to train the model, as well as the type of model that will be trained. You may issue bob tb train --help for a help message containing more detailed instructions.

Examples

To train a logistic regressor using predictions from DensenetForRS on the Montgomery dataset:

$ bob tb train -vv logistic_regression montgomery_rs --batch-size=4 --epochs=20

To train Signs_to_TB using predictions from DensenetForRS on the Shenzhen dataset:

$ bob tb train -vv signs_to_tb shenzhen_rs --batch-size=4 --epochs=20