set_random_seed(12345)
model.unfreeze()
optimizer = optim.SGD(model.get_trainable_parameters(),
lr=model.lr,
momentum=model.momentum,
weight_decay=model.weight_decay,
nesterov=True)
scheduler = step_decay_scheduler(optimizer,
steps=model.steps,
scales=model.scales)
losses = model.fit(train_data=train_data,
val_data=val_data,
optimizer=optimizer,
scheduler=scheduler,
epochs=120,
checkpoint_frequency=120,
num_workers=8)
pickle.dump(losses, open('{}_losses.pkl'.format(model.name), 'wb'))
if predict:
set_random_seed(12345)
model.predict(dataset=test_data,
confidence_threshold=.001,
overlap_threshold=.45,
show=False,
export=True)
model.mini_freeze()
optimizer = optim.SGD(model.get_trainable_parameters(),
lr=model.lr,
momentum=model.momentum,
weight_decay=model.weight_decay,
nesterov=True)
scheduler = step_decay_scheduler(optimizer,
steps=model.steps,
scales=model.scales)
losses = model.fit(train_data=train_data,
val_data=val_data,
optimizer=optimizer,
scheduler=scheduler,
epochs=120,
checkpoint_frequency=120,
num_workers=8)
pickle.dump(losses, open('{}_losses.pkl'.format(model.name), 'wb'))
if predict:
set_random_seed(12345)
model.load_weights('models/yolov2-tiny-voc.weights')
predictions = model.predict(dataset=test_data,
confidence_threshold=.5,
overlap_threshold=.45,
show=False,
export=False)