Usage

Create Dataset for fast training

The following command will create the dataset cvc_clinicdb in /tmp/data and the records in /tmp/data/cvc_clinicdb/records.

python -m tf_semantic_segmentation.bin.tfrecord_writer -d cvc_clinicdb -c '/tmp/data'

For using your own dataset please refer to the datasets section.

Training

Hint: To see train/test/val images you have to start tensorboard like this

tensorboard --logdir=logs/ --reload_multifile=true

On inbuild datasets - generator (slow)

python -m tf_semantic_segmentation.bin.train -ds 'cvc_clinicdb' -bs 8 -e 100 \
    -logdir 'logs/taco-binary-test' -o 'adam' -lr 5e-3 --size 256,256 \
    -l 'binary_crossentropy' -fa 'sigmoid' \
    --train_on_generator --gpus='0' \
    --tensorboard_train_images --tensorboard_val_images

Using a fixed record path (fast)

python -m tf_semantic_segmentation.bin.train --record_dir="/tmp/data/cvc_clinicdb/records" \
    -bs 4 -e 100 -logdir 'logs/cvc-adam-1e-4-mish-bs4' -o 'adam' -lr 1e-4 -l 'categorical_crossentropy' \
    -fa 'softmax' -bufsize 50 --metrics='iou_score,f1_score' -m 'unet' --gpus='0' -a 'mish' \
    --tensorboard_train_images --tensorboard_val_images

Multi GPU training

python -m tf_semantic_segmentation.bin.train --record_dir="/tmp/data/cvc_clinicdb/records" \
    -bs 4 -e 100 -logdir 'logs/cvc-adam-1e-4-mish-bs4' -o 'adam' -lr 1e-4 -l 'categorical_crossentropy' \
    -fa 'softmax' -bufsize 50 --metrics='iou_score,f1_score' -m 'unet' --gpus='0,1,2,3' -a 'mish'

Using Code

from tf_semantic_segmentation.bin.train import train_test_model, get_args

# get the default args
args = get_args({})

# change some parameters
# !rm -r logs/
args.model = 'unet'
# args['color_mode'] = 0
args.batch_size = 8
args.size = [128, 128] # resize input dataset to this size
args.epochs = 10
args.learning_rate = 1e-4
args.optimizer = 'adam' # ['adam', 'radam', 'ranger']
args.loss = 'dice'
args.logdir = 'logs'
args.record_dir = "/tmp/data/cvc_clinicdb/records"
args.final_activation = 'softmax'

# train and test
results, model = train_test_model(args)
results['evaluate'] # returns last evaluated results using val dataset
results['history'] # returns the history object from model.fit()