mask_rcnn_damage_0010.h5

[vajpaye]

Hi @priya-dwivedi

can you please tell me where can i download this "mask_rcnn_damage_0010.h5" file

[soufianesabiri]

I have same problem please help

[vajpaye]

Hi @soufianesabiri

may I know in what context you are trying to apply this?

[vajpaye]

if we are trying to apply this for car damage detection or in any context follow these steps please: 1.Use the mask_rcnn_coo.h5 https://github.com/matterport/Mask_RCNN/releases 2.train the model with mask_rcnn_coo.h5 3.after training you can save your file to your directory. model_path = os.path.join(ROOT_DIR, "mask_rcnn_damage_0010.h5") model.keras_model.save_weights(model_path)

[soufianesabiri]

thanks for your answer, how to train the model please?? I have installed tensorflow and keras.

[vajpaye]

import os import sys import itertools import math import logging import json import re import random from collections import OrderedDict import numpy as np import matplotlib import matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.lines as lines from matplotlib.patches import Polygon from keras.optimizers import SGD, Adam, RMSprop

Root directory of the project

ROOT_DIR = ROOT_DIR = os.path.abspath("../../")

Import Mask RCNN

sys.path.append(ROOT_DIR) # To find local version of the library print(ROOT_DIR) from mrcnn import utils from mrcnn import visualize from mrcnn.visualize import display_images import mrcnn.model as modellib from mrcnn.model import log MODEL_DIR = os.path.join(ROOT_DIR, "logs") print(MODEL_DIR) import car_damage

%matplotlib inline COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") print(COCO_MODEL_PATH)

Download COCO trained weights from Releases if needed

if not os.path.exists(COCO_MODEL_PATH): utils.download_trained_weights(COCO_MODEL_PATH)

BATCH_SIZE = 128 NB_EPOCH = 2 NB_CLASSES = 1 VERBOSE = 1 VALIDATION_SPLIT = 0.2 OPTIM = RMSprop()

config = car_damage.damageConfig() CUSTOM_DIR = os.path.join(ROOT_DIR, "customImages") print(CUSTOM_DIR)

class InferenceConfig(config.class):

Run detection on one image at a time

GPU_COUNT = 1
IMAGES_PER_GPU = 1

config = InferenceConfig() config.display()

def get_ax(rows=1, cols=1, size=16): """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes.

Adjust the size attribute to control how big to render images
"""
_, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
return ax

Load dataset

Get the dataset from the releases page

https://github.com/matterport/Mask_RCNN/releases

dataset_train = car_damage.damageDataset() print(CUSTOM_DIR) dataset_train.load_damage(CUSTOM_DIR, "train")

Must call before using the dataset

dataset_train.prepare()

print("Image Count: {}".format(len(dataset_train.image_ids))) print("Class Count: {}".format(dataset_train.num_classes)) for i, info in enumerate(dataset_train.class_info): print("{:3}. {:50}".format(i, info['name']))

Load dataset

Get the dataset from the releases page

https://github.com/matterport/Mask_RCNN/releases

dataset_val = car_damage.damageDataset() dataset_val.load_damage(CUSTOM_DIR, "val")

Must call before using the dataset

dataset_val.prepare()

print("Image Count: {}".format(len(dataset_val.image_ids))) print("Class Count: {}".format(dataset_val.num_classes)) for i, info in enumerate(dataset_val.class_info): print("{:3}. {:50}".format(i, info['name']))

image_ids = np.random.choice(dataset_train.image_ids, 4) for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names) print(image_id) print(dataset_train.source_class_ids)

print(image_path)

print(dataset_train.image_info[image_id]["source"])
print(dataset_train.source_class_ids[dataset_train.image_info[image_id]["source"]])

print(MODEL_DIR)

model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR)

init_with = "coco" # imagenet, coco, or last

if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True) elif init_with == "coco":

Load weights trained on MS COCO, but skip layers that

# are different due to the different number of classes
# See README for instructions to download the COCO weights
model.load_weights(COCO_MODEL_PATH, by_name=True,
                   exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", 
                            "mrcnn_bbox", "mrcnn_mask"])

elif init_with == "last":

Load the last model you trained and continue training

model.load_weights(model.find_last(), by_name=True)

Train the head branches

Passing layers="heads" freezes all layers except the head

layers. You can also pass a regular expression to select

which layers to train by name pattern.

model.train(dataset_train, dataset_val, learning_rate=config.LEARNING_RATE, epochs=1, layers='heads')

model_path = os.path.join(ROOT_DIR, "mask_rcnn_damage.h5") model.keras_model.save_weights(model_path)

[soufianesabiri]

Thanks a lot! I will try this right after.

[vajpaye]

even you can refer this link

https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb

[soufianesabiri]

thanks!

[girijesh18]

Hey @vajpaye In Import Mask RCNN import car_damage is giving error that No module named 'car_damage' can you please help to figure it out. Thanks

[angyee]

ValueError: Layer #389 (named "mrcnn_bbox_fc"), weight <tf.Variable 'mrcnn_bbox_fc/kernel:0' shape=(1024, 8) dtype=float32_ref> has shape (1024, 8), but the saved weight has shape (1024, 324)

@vajpaye

[girijesh18]

@angyee you need to ADD few lines to else condition

print("Loading weights ", weights_path) if args.weights.lower() == "coco":

Exclude the last layers because they require a matching

# number of classes
model.load_weights(weights_path, by_name=True, exclude=[
    "mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"])

else: model.load_weights(weights_path, by_name=True, exclude=[ "mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"])

[angyee]

done exactly same but the result is saving in png image file but no masked image result for a damaged part on the car

@girijesh18

[girijesh18]

@angyee I am facing the same issue. Currently, I am working on it and will let you know if I would resolve it. Please let me know if you get some lead in this

[girijesh18]

@angyee I am able to run the algorithm successfully but still, the trained weights are not able to generate a splash. I think it needs more data and a higher number of epochs to generate the splash. Let me know if you are facing any issues to run this algorithm.

[ritikgarg655]

import os import sys import itertools import math import logging import json import re import random from collections import OrderedDict import numpy as np import matplotlib import matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.lines as lines from matplotlib.patches import Polygon from keras.optimizers import SGD, Adam, RMSprop

Root directory of the project

ROOT_DIR = ROOT_DIR = os.path.abspath("../../")

Import Mask RCNN

sys.path.append(ROOT_DIR) # To find local version of the library print(ROOT_DIR) from mrcnn import utils from mrcnn import visualize from mrcnn.visualize import display_images import mrcnn.model as modellib from mrcnn.model import log MODEL_DIR = os.path.join(ROOT_DIR, "logs") print(MODEL_DIR) import car_damage

%matplotlib inline COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") print(COCO_MODEL_PATH)

Download COCO trained weights from Releases if needed

if not os.path.exists(COCO_MODEL_PATH): utils.download_trained_weights(COCO_MODEL_PATH)

BATCH_SIZE = 128 NB_EPOCH = 2 NB_CLASSES = 1 VERBOSE = 1 VALIDATION_SPLIT = 0.2 OPTIM = RMSprop()

config = car_damage.damageConfig() CUSTOM_DIR = os.path.join(ROOT_DIR, "customImages") print(CUSTOM_DIR)

class InferenceConfig(config.class):

Run detection on one image at a time

GPU_COUNT = 1 IMAGES_PER_GPU = 1

config = InferenceConfig() config.display()

def get_ax(rows=1, cols=1, size=16): """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes.

Adjust the size attribute to control how big to render images
"""
_, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
return ax

Load dataset

Get the dataset from the releases page

https://github.com/matterport/Mask_RCNN/releases

dataset_train = car_damage.damageDataset() print(CUSTOM_DIR) dataset_train.load_damage(CUSTOM_DIR, "train")

Must call before using the dataset

dataset_train.prepare()

print("Image Count: {}".format(len(dataset_train.image_ids))) print("Class Count: {}".format(dataset_train.num_classes)) for i, info in enumerate(dataset_train.class_info): print("{:3}. {:50}".format(i, info['name']))

Load dataset

Get the dataset from the releases page

https://github.com/matterport/Mask_RCNN/releases

dataset_val = car_damage.damageDataset() dataset_val.load_damage(CUSTOM_DIR, "val")

Must call before using the dataset

dataset_val.prepare()

print("Image Count: {}".format(len(dataset_val.image_ids))) print("Class Count: {}".format(dataset_val.num_classes)) for i, info in enumerate(dataset_val.class_info): print("{:3}. {:50}".format(i, info['name']))

image_ids = np.random.choice(dataset_train.image_ids, 4) for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names) print(image_id) print(dataset_train.source_class_ids)

print(image_path)

print(dataset_train.image_info[image_id]["source"]) print(dataset_train.source_class_ids[dataset_train.image_info[image_id]["source"]])

print(MODEL_DIR)

model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR)

init_with = "coco" # imagenet, coco, or last

if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True) elif init_with == "coco":

Load weights trained on MS COCO, but skip layers that

are different due to the different number of classes

See README for instructions to download the COCO weights

model.load_weights(COCO_MODEL_PATH, by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"]) elif init_with == "last":

Load the last model you trained and continue training

model.load_weights(model.find_last(), by_name=True)

Train the head branches

Passing layers="heads" freezes all layers except the head

layers. You can also pass a regular expression to select

which layers to train by name pattern.

model.train(dataset_train, dataset_val, learning_rate=config.LEARNING_RATE, epochs=1, layers='heads')

model_path = os.path.join(ROOT_DIR, "mask_rcnn_damage.h5") model.keras_model.save_weights(model_path)

@vajpaye I am facing error in save_weights: TypeError: can't pickle _thread.RLock objects mrcnn

[achieverprince]

Same here, result image is same as input. This never works