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Added possibility to save model based on Val_loss in custom Detection #501

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Added possibility to save model based on Val_loss in custom Detection #501

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Added possibility to save model based on Val_loss in custom Detection
bkazour Mar 7, 2020
45ef364
Add a minimal value to loss so that it won't go to NaN in case loss i…
bkazour Mar 7, 2020
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10 changes: 5 additions & 5 deletions imageai/Detection/Custom/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,7 @@ def setTrainConfig(self, object_names_array, batch_size=4, num_experiments=100,

print("Detection configuration saved in ", os.path.join(self.__json_directory, "detection_config.json"))

def trainModel(self):
def trainModel(self, mode='min', monitor='loss'):

"""
'trainModel()' function starts the actual model training. Once the training starts, the training instance
Expand Down Expand Up @@ -278,7 +278,7 @@ def trainModel(self):
###############################
# Kick off the training
###############################
callbacks = self._create_callbacks(self.__train_weights_name, infer_model)
callbacks = self._create_callbacks(self.__train_weights_name, infer_model, mode=mode, monitor=monitor)

train_model.fit_generator(
generator=train_generator,
Expand Down Expand Up @@ -483,15 +483,15 @@ def _create_training_instances(self,

return train_ints, valid_ints, sorted(labels), max_box_per_image

def _create_callbacks(self, saved_weights_name, model_to_save):
def _create_callbacks(self, saved_weights_name, model_to_save, mode='min', monitor='loss'):

checkpoint = CustomModelCheckpoint(
model_to_save=model_to_save,
filepath=saved_weights_name + 'ex-{epoch:03d}--loss-{loss:08.3f}.h5',
monitor='loss',
monitor= monitor,
verbose=0,
save_best_only=True,
mode='min',
mode= mode,
period=1
)
reduce_on_plateau = ReduceLROnPlateau(
Expand Down
2 changes: 1 addition & 1 deletion imageai/Detection/Custom/yolo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,7 +177,7 @@ def call(self, x):
loss = loss_xy + loss_wh + loss_conf + loss_class


return loss*self.grid_scale
return loss*self.grid_scale + 1e-20

def compute_output_shape(self, input_shape):
return [(None, 1)]
Expand Down