def train_yolo(lst_y, train_path="fs/training_yolo/", transfer_modelfile=""):
## setup
yolo = DetectionModelTrainer()
yolo.setModelTypeAsYOLOv3()
yolo.setDataDirectory(data_directory=train_path)
yolo.setTrainConfig(object_names_array=lst_y,
batch_size=4,
num_experiments=1,
train_from_pretrained_model=transfer_modelfile)
## train
print("--- training ---")
yolo.trainModel()
## evaluate
print("--- metrics ---")
metrics = yolo.evaluateModel(model_path=train_path + "models",
json_path=train_path +
"json/detection_config.json",
iou_threshold=0.5,
object_threshold=0.5,
nms_threshold=0.5)
print(metrics)
## laod model
print("--- loading model ---")
modelfile = os.listdir(train_path + "models/")[0]
return load_yolo(modelfile=train_path + "models/" + modelfile,
confjson=train_path + "/json/detection_config.json")
def train_model():
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="examples/specjale")
trainer.setTrainConfig(object_names_array=["specjal"], batch_size=4, num_experiments=10,
train_from_pretrained_model="models/yolo.h5")
trainer.trainModel()
def entrenarModelo():
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="2_RESPUESTA")
trainer.setTrainConfig(object_names_array=["RESPUESTA"],
batch_size=2,
num_experiments=50,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
def train(self, data_dir, classes, batch_size, epochs):
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=data_dir)
trainer.setTrainConfig(object_names_array=classes,
batch_size=batch_size,
num_experiments=epochs,
train_from_pretrained_model=PRETRAINED_MODEL)
trainer.trainModel()
def train_detection_model():
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="fire-dataset")
trainer.setTrainConfig(object_names_array=["fire"], batch_size=8, num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
class Trainer():
def __init__(self):
self.trainer = DetectionModelTrainer()
self.trainer.setModelTypeAsYOLOv3()
# Set name of dataset directory
self.trainer.setDataDirectory(data_directory="dog_dataset")
self.trainer.setTrainConfig(object_names_array=['dog'], batch_size=4, num_experiments=10, train_from_pretrained_model='yolov3-dogs.h5')
def train(self):
self.trainer.trainModel()
class Trainer():
def __init__(self):
self.trainer = DetectionModelTrainer()
self.trainer.setModelTypeAsYOLOv3()
# Set name of dataset directory
self.trainer.setDataDirectory(data_directory="wheels_dataset")
self.trainer.setTrainConfig(object_names_array=['wheelchair', 'pull trolley', 'push trolley'], batch_size=8, num_experiments=10, train_from_pretrained_model='wheels-yolov3.h5')
def train(self):
self.trainer.trainModel()
def trainer():
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=data_directory)
trainer.setTrainConfig(
object_names_array=["seal", "no_seal"],
batch_size=4,
num_experiments=50,
train_from_pretrained_model="pretrained-yolov3.h5",
)
trainer.trainModel()
def train():
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=os.path.join('drive','MyDrive','train_data', 'circle'))
trainer.setTrainConfig(
object_names_array=['circle'],
batch_size=4,
num_experiments=200,
train_from_pretrained_model='pretrained-yolov3.h5'
)
trainer.trainModel()
def train_detection_model():
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="fire-dataset")
trainer.setTrainConfig(object_names_array=["fire"], batch_size=8, num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5")
# download 'pretrained-yolov3.h5' from the link below
# https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5
trainer.trainModel()
def train():
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="")
trainer.setTrainConfig(
object_names_array=["Container", "Dent", "Rust", "Crack"],
batch_size=4,
num_experiments=100,
train_from_pretrained_model="pretrained-yolov3.h5",
)
trainer.trainModel()
def trainer(
data_directory="plates",
object_names_array=["LP"],
batch_size=4,
num_experiments=2,
train_from_pretrained_model="plates/models/detection_model-ex-002--loss-0007.222.h5"
):
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory)
trainer.setTrainConfig(object_names_array, batch_size, num_experiments,
train_from_pretrained_model)
trainer.trainModel()
def test_detection_training(clear_keras_session):
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=sample_dataset)
trainer.setTrainConfig(object_names_array=["hololens"], batch_size=2, num_experiments=1, train_from_pretrained_model=pretrained_model)
trainer.trainModel()
assert os.path.isdir(sample_dataset_json_folder)
assert os.path.isdir(sample_dataset_models_folder)
assert os.path.isdir(sample_dataset_cache_folder)
assert os.path.isfile(os.path.join(sample_dataset_json_folder, "detection_config.json"))
shutil.rmtree(os.path.join(sample_dataset_json_folder))
shutil.rmtree(os.path.join(sample_dataset_models_folder))
shutil.rmtree(os.path.join(sample_dataset_cache_folder))
def keurmerk_train(data, model):
"""Trains YOLOv3 model from pretrained model.
Arguments:
data {string} -- Directory where the image and annotation data is
stored according to the specified structure.
model {string} -- Directory where the chosen model is stored.
"""
# Set model type
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
labels = [
"fairtrade",
"planetproof",
"utz",
"organic",
"vegan",
"weidemelk",
"bio",
"eko",
"asc",
"msc",
"ebio",
"beterleven",
]
# Data folder
trainer.setDataDirectory(data_directory=DATA_DIR)
# Training configuration
trainer.setTrainConfig(
object_names_array=labels,
batch_size=4,
num_experiments=100,
train_from_pretrained_model=MODEL_PATH,
)
trainer.trainModel()
def train_model(self,
data_dir,
user,
objs_array,
model=None,
new_model=False):
"""
Train a specific model according with a set of data
Args:
data_dir (str): directory with the YOLO training data (pictures and annotations)
user (str): registered user which the model belongs
objs_array (list): list containing the names of unique objects to be trained for
model (str): full path of the model file to be loaded
new_model (bool): True if after training a new model file will be generated
Returns:
object: trained model
"""
if new_model:
model_path = ''
else:
self.load_model(user=user, model_path=model)
model_path = self.model_path
model_trainer = DetectionModelTrainer()
model_trainer.setModelTypeAsYOLOv3()
model_trainer.setDataDirectory(data_directory=data_dir)
model_trainer.setTrainConfig(object_names_array=list(set(objs_array)),
batch_size=4,
num_experiments=200,
train_from_pretrained_model=model_path)
model_trainer.trainModel()
if new_model:
self.version_model(user=user)
self.model = model_trainer
return self.model
def train_model(self,
model_pretrained,
label_list,
batch_size=2,
epoch=10,
use_pretrained_model=False):
self.__use_pretrained_model = use_pretrained_model
if self.__use_pretrained_model:
trainer = DetectionModelTrainer()
# fixe le type du model comme etant yoloV3
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(self.__data_dir)
trainer.setTrainConfig(
object_names_array=label_list,
batch_size=batch_size,
num_experiments=epoch,
train_from_pretrained_model=model_pretrained)
trainer.trainModel()
def main():
# Will we train?
if DOTRAIN:
# Use last trained model as base for transferlearning?
last_model = '' # Ohmy. This means None.
if DOTRANSFERLEARN:
last_model = MODEL_PATH
if DORESUMELAST:
last_model = get_last_model()
else:
if os.path.exists(os.path.join(DATA_PATH, 'cache')):
shutil.rmtree(os.path.join(DATA_PATH, 'cache'))
shutil.rmtree(os.path.join(DATA_PATH, 'json'))
shutil.rmtree(os.path.join(DATA_PATH, 'logs'))
shutil.rmtree(os.path.join(DATA_PATH, 'models'))
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=DATA_PATH)
trainer.setTrainConfig(object_names_array=OBJECT_NAMES,
batch_size=BATCHCOUNT,
num_experiments=EXPIREMENTSCOUNT,
train_from_pretrained_model=last_model)
trainer.trainModel()
# Will we evaluate?
if DOEVALUATE:
# Show evaluations for the last model
metrics = trainer.evaluateModel(model_path=get_last_model(),
json_path=os.path.join(
DATA_PATH, 'json',
'detection_config.json'),
iou_threshold=.5,
object_threshold=.3,
nms_threshold=.5)
print(metrics)
def main():
if (len(sys.argv) < 3):
print("Usage: " + sys.argv[0] + " <dataset_dir> <pre_trained_model>")
exit(-1)
dataset_dir = sys.argv[1]
pre_trained_model = sys.argv[2]
if (len(sys.argv) > 2):
image_name = sys.argv[2]
else:
print("Enter image or directory name: ")
image_name = input()
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory=dataset_dir)
trainer.setTrainConfig(object_names_array=[
"Ripe Strawberry", "Green Strawberry", "Bad Strawberry"
],
batch_size=4,
num_experiments=40,
train_from_pretrained_model=pre_trained_model)
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
trainer = DetectionModelTrainer()
trainer.setModelTypeAsYOLOv3()
trainer.setDataDirectory(data_directory="bottles")
trainer.setTrainConfig(
object_names_array=["PET", "HDPE(1)", "HDPE(2)", "PVC", "Opaque", "PP"],
batch_size=2,
num_experiments=200,
train_from_pretrained_model="pretrained-yolov3.h5")
trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer Model_trainer = DetectionModelTrainer() Model_trainer.setModelTypeAsYOLOv3() Model_trainer.setDataDirectory(data_directory="apple_dataset") Model_trainer.setTrainConfig(object_names_array=["apple", "damaged_apple"], batch_size=8, num_experiments=50, train_from_pretrained_model="pretrained-yolov3.h5") Model_trainer.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
manaf = DetectionModelTrainer()
manaf.setModelTypeAsYOLOv3()
manaf.setDataDirectory(data_directory="AI-ny")
manaf.setTrainConfig(object_names_array=["Kartong", "Plast", "Metall"],
batch_size=2,
num_experiments=180,
train_from_pretrained_model="pretrained-yolov3.h5")
manaf.trainModel()
from imageai.Detection.Custom import DetectionModelTrainer
# Initialize the model trainer and set it to YOLO (YOLOv3 is optional)
train = DetectionModelTrainer()
train.setModelTypeAsYOLOv3()
# Choose the name of your directory
d_name = "tires"
train.setDataDirectory(data_directory=d_name)
train.setTrainConfig(object_names_array=[d_name],
batch_size=4,
num_experiments=203)
train.trainModel()
# get the absolute image path for the image from the annotation
img_abs_path = os.path.join(images_directory, img_filename)
if os.path.isfile(img_abs_path):
# image corresponding to the annotation exists
# print(img_abs_path + " exists")
pass
else:
print(img_abs_path + " does not exists")
os.remove(abs_annotation_file_path)
print(abs_annotation_file_path + " Deleted")
print(count)
!wget https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5 -P '/gdrive/My Drive/BE Project 2019 - 2020/final_training_folder/models/'
import os
from imageai.Detection.Custom import DetectionModelTrainer
model_trainer = DetectionModelTrainer()
model_trainer.setModelTypeAsYOLOv3()
model_trainer.setDataDirectory(data_directory_path)
yolo_path = os.path.join(os.path.join(execution_path, "models"), "pretrained-yolov3.h5")
model_trainer.setTrainConfig(object_names_array=["glass", "metal", "paper", "plastic"], batch_size=10, num_experiments=50, train_from_pretrained_model=yolo_path)
model_trainer.trainModel()
