Mask_rcnn: error: the following arguments are required: --weights
I am trying to run the balloon.py and am getting this error. Please advice as to how this can be resolved.
Thanks
artisvirat
All 18 comments
Never mind resolved it myself. Stupid error
artisvirat
on 20 Jul 2018
@artisvirat I get the error too. How do you solve it ?
GeneralLi95
on 25 Jul 2018
change --model to --weights in command line arguement
artisvirat
on 26 Jul 2018
@artisvirat I'm not able to find --model. I'm getting the same error "ipykernel_launcher.py: error: the following arguments are required: --weights"
How solve it?
siddheshgunjal
on 9 Aug 2018
For once and all:
change --model to --weights. Example:
--model=coco to --weights=coco
coco is the path to the mask_rcnn_coco.h5 file.
artisvirat
on 10 Aug 2018
Hi All,
Same error here, I am not able to make this run! Can you please override the change required on the script below?
if __name__ == '__main__':
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train Mask R-CNN to detect balloons.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="/path/to/balloon/dataset/",
help='Directory of the Balloon dataset')
parser.add_argument('--weighs', required=True,
metavar="/path/to/weights.h5",
help="Path to weights .h5 file or 'coco'")
parser.add_argument('--logs', required=False,
default=DEFAULT_LOGS_DIR,
metavar="/path/to/logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument('--image', required=False,
metavar="path or URL to image",
help='Image to apply the color splash effect on')
parser.add_argument('--video', required=False,
metavar="path or URL to video",
help='Video to apply the color splash effect on')
args = parser.parse_args()
Many thanks!
AlbertoMCS
on 26 Mar 2019
@AlbertoMCS hi. try to change here. it helped me.
flit86
on 12 Jun 2019
The error still does not change, does anyone can help? many thanks.
if __name__ == '__main__':
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train Mask R-CNN to detect balloons.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="_dataset",
help='Directory of the Balloon dataset')
parser.add_argument('--weights', required=True,
metavar=r"C:\Users\SOS\Desktop\matterport\Mask_RCNN\mask_rcnn_coco.h5",
help="Path to weights .h5 file or 'coco'")
parser.add_argument('--logs', required=False,
default=DEFAULT_LOGS_DIR,
metavar="/path/to/logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument('--image', required=False,
metavar="path or URL to image",
help='Image to apply the color splash effect on')
parser.add_argument('--video', required=False,
metavar="path or URL to video",
help='Video to apply the color splash effect on')
args = parser.parse_args()
the Error: balloon.py: error: the following arguments are required:
YongLAGCC
on 26 Jul 2019
Anyone please help to solve the same error.
Thank you.
Ayushi122
on 8 Jan 2020
you can watch this video to solve your problem:
https://www.youtube.com/watch?v=XYUXFR5FSxI
Aldhubri
on 16 Jan 2020
@Aldhubri thank you so much. Yea. I saw still the same error.
Ayushi122
on 16 Jan 2020
For once and all:
change --model to --weights. Example:
--model=coco to --weights=coco
coco is the path to the mask_rcnn_coco.h5 file.
Hello, I have such a problem:
the following arguments are required:
How to solve it? I haven't changed anything
xywzhht
on 16 Jan 2020
Hello, everyone. Do you know what my mistake is? thank you very much indeed.
F:\Python3.5.0\python.exe D:/read/match_rcnn-master/main.py
Using TensorFlow backend.
usage: main.py [-h] [--dataset ./img/] --weights
D:/read/match_rcnn-master/mask_rcnn_coco.h5 [--logs ./logs/]
[--image path or URL to image] [--video path or URL to video]
<command>
main.py: error: the following arguments are required: <command>, --weights
My configuration:
`if __name__ == "__main__":
ROOT_DIR = os.path.abspath("./")
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
COCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train Mask R-CNN for DeepFashion.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="./img/",
help='Directory of the Mask rcnn dataset')
parser.add_argument('--weights',required=True,
#default=COCO_WEIGHTS_PATH,
metavar="D:/read/match_rcnn-master/mask_rcnn_coco.h5",
help="Path to weights .h5 file or 'coco'")
parser.add_argument('--logs', required=False,
default=DEFAULT_LOGS_DIR,
metavar="./logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument('--image', required=False,
metavar="path or URL to image",
help='Image to apply the color splash effect on')
parser.add_argument('--video', required=False,
metavar="path or URL to video",
help='Video to apply the color splash effect on')
args = parser.parse_args()`
can you try to replace command with --command
Aldhubri
on 16 Jan 2020
import argparse
import numpy as np
from keras.models import load_model
from keras.preprocessing import image
from evaluate_model import evaluate_model, extract_hard_samples
from transfer_learning import train_simpler_inception_based_model
from video_annotation import video_fire_detection
from keras.applications.inception_v3 import preprocess_input as inception_preprocess_input
if __name__ == '__main__':
classes = ['fire', 'no_fire', 'start_fire']
parser = argparse.ArgumentParser(description='Convolutional neural network for forest fire detection',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
subparsers = parser.add_subparsers(title='',
description='Network can be trained on a provided dataset or predictions can be'
'made using a pre-trained model. Models can also be evaluated.',
help='', dest='mode')
subparsers.required = True
parser_train = subparsers.add_parser('train',
help='Create and train the simpler InceptionV3-based model.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_train.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to the dataset on which to train.',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-prop',
type=float,
action='store',
dest='proportion',
help='Proportion of the dataset to be used for training (the rest is for validation).',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-freeze',
type=bool,
action='store',
dest='freeze',
help='Whether to freeze every layer except the last fully connected ones.',
default=argparse.SUPPRESS,
required=True)
parser_train.add_argument('-epochs',
type=int,
action='store',
dest='epochs',
help='Number of epochs.',
default=10,
required=False)
parser_train.add_argument('-batch',
type=int,
action='store',
dest='batch_size',
help='Size of a batch.',
default=32,
required=False)
parser_tune = subparsers.add_parser('tune', help='Fine-tune a pre-trained Inception-V3-based model.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_tune.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to the pre-trained model.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-lr',
type=float,
action='store',
dest='learning_rate',
help='Learning rate to be used for fine-tuning.',
default=0.001,
required=False)
parser_tune.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to the dataset on which to train.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-prop',
type=float,
action='store',
dest='proportion',
help='Proportion of the dataset to be used for training (the rest is for validation).',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-freeze',
type=bool,
action='store',
dest='freeze',
help='Whether to freeze every layer except the last fully connected ones.',
default=argparse.SUPPRESS,
required=True)
parser_tune.add_argument('-epochs',
type=int,
action='store',
dest='epochs',
help='Number of epochs.',
default=10,
required=False)
parser_tune.add_argument('-batch',
type=int,
action='store',
dest='batch_size',
help='Size of a batch.',
default=32,
required=False)
parser_predict = subparsers.add_parser('predict',
help='Perform prediction on a provided picture.')
parser_predict.add_argument('-path',
type=str,
action='store',
dest='image_path',
help='Path to an image.',
default=argparse.SUPPRESS,
required=True)
parser_predict.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_video = subparsers.add_parser('video',
help='Perform prediction on a video.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser_video.add_argument('-in',
type=str,
action='store',
dest='input_video_path',
help='Path to an mp4 video.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-out',
type=str,
action='store',
dest='output_video_path',
help='Path to output the annotated mp4 video.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_video.add_argument('-freq',
type=int,
action='store',
dest='freq',
help='Prediction is to be made every freq frames.',
default=12,
required=False)
parser_extract = subparsers.add_parser('extract',
help='Extract hard examples from a dataset (samples classified with low '
'confidence).')
parser_extract.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to a dataset.',
default=argparse.SUPPRESS,
required=True)
parser_extract.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parser_extract.add_argument('-threshold',
type=float,
action='store',
dest='extract_threshold',
help='Threshold for the hard examples.',
default=argparse.SUPPRESS,
required=True)
parser_test = subparsers.add_parser('test',
help='Test a model on a test set of images.')
parser_test.add_argument('-data',
type=str,
action='store',
dest='dataset',
help='Path to a test set.',
default=argparse.SUPPRESS,
required=True)
parser_test.add_argument('-model',
type=str,
action='store',
dest='model_path',
help='Path to a trained model.',
default=argparse.SUPPRESS,
required=True)
parsed = parser.parse_args()
if parsed.mode == "train":
train_simpler_inception_based_model(parsed.dataset,
fine_tune_existing=None,
freeze=parsed.freeze,
learning_rate=0.001,
percentage=parsed.proportion,
nbr_epochs=parsed.epochs,
batch_size=parsed.batch_size)
elif parsed.mode == "tune":
train_simpler_inception_based_model(parsed.dataset,
fine_tune_existing=parsed.model_path,
freeze=parsed.freeze,
learning_rate=parsed.learning_rate,
percentage=parsed.proportion,
nbr_epochs=parsed.epochs,
batch_size=parsed.batch_size)
elif parsed.mode == "predict":
model = load_model(parsed.model_path)
img = image.load_img(parsed.image_path, target_size=(224, 224, 3))
# processed image to feed the network
processed_img = image.img_to_array(img)
processed_img = np.expand_dims(processed_img, axis=0)
processed_img = inception_preprocess_input(processed_img)
# get prediction using the network
predictions = model.predict(processed_img)[0]
print(predictions)
elif parsed.mode == "video":
video_fire_detection(parsed.input_video_path,
parsed.output_video_path,
parsed.model_path,
inception_preprocess_input,
(224, 224),
parsed.freq)
elif parsed.mode == "extract":
print(extract_hard_samples(parsed.model_path,
inception_preprocess_input,
parsed.dataset,
parsed.extract_threshold))
elif parsed.mode == "test":
print(evaluate_model(parsed.model_path,
classes,
inception_preprocess_input,
parsed.dataset,
parsed.extract_threshold))
khamamsoulef
on 25 Feb 2020
error Reloaded modules: evaluate_model, setup, setup.naive_approach, transfer_learning, video_annotation
usage: launcher.py [-h] {train,tune,predict,video,extract,test} ...
launcher.py: error: the following arguments are required: mode
An exception has occurred, use %tb to see the full traceback.
khamamsoulef
on 25 Feb 2020
@khamamsoulef
Even i have the same error.
Is your problem solved?
if yes, can u please help me with that
srivaish-11
on 9 Mar 2020
use this command python balloon.py train --dataset=/path/to/balloon/dataset --weights=coco
thief12318
on 14 Mar 2020
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