Qiita Teams that are logged in
You are not logged in to any team

Log in to Qiita Team
Community
OrganizationEventAdvent CalendarQiitadon (β)
Service
Qiita JobsQiita ZineQiita Blog
0
Help us understand the problem. What is going on with this article?

More than 3 years have passed since last update.

@ohisama@github

ラズパイでkerasのimagenetモデルを使ってみる。

概要

ラズパイでkerasのimagenetモデルを使ってみる。

写真

Xceptionの場合

('Predicted:', [(u'n04179913', u'sewing_machine', 1.0), (u'n15075141', u'toilet_tissue', 0.0), (u'n02317335', u'starfish', 0.0)])
Time elapsed: 88

サンプルコード

from tensorflow.contrib.keras.python.keras.applications.xception import Xception
from tensorflow.contrib.keras.python.keras.preprocessing import image
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import preprocess_input, decode_predictions
import numpy as np
import time

img_path = 'p1m.jpg'
img = image.load_img(img_path, target_size = (299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x = preprocess_input(x)
begin = time.clock()
model = Xception(weights = 'imagenet')
preds = model.predict(x)
print ('Predicted:', decode_predictions(preds, top = 3)[0])
print ('Time elapsed: %.0f' % (time.clock() - begin))

ResNet50の場合

('Predicted:', [(u'n02098286', u'West_Highland_white_terrier', 1.0), (u'n15075141', u'toilet_tissue', 0.0), (u'n02319095', u'sea_urchin', 0.0)])
Time elapsed: 94

サンプルコード

from tensorflow.contrib.keras.python.keras.applications.resnet50 import ResNet50
from tensorflow.contrib.keras.python.keras.preprocessing import image
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import preprocess_input, decode_predictions
import numpy as np
import time

img_path = 'p1m.jpg'
img = image.load_img(img_path, target_size = (224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x = preprocess_input(x)
begin = time.clock()
model = ResNet50(weights = 'imagenet')
preds = model.predict(x)
print ('Predicted:', decode_predictions(preds, top = 3)[0])
print ('Time elapsed: %.0f' % (time.clock() - begin))

InceptionV3の場合

('Predicted:', [(u'n01924916', u'flatworm', 0.7561847), (u'n04328186', u'stopwatch', 0.21795489), (u'n06359193', u'web_site', 0.01172213)])
Time elapsed: 123

サンプルコード

from tensorflow.contrib.keras.python.keras.applications.inception_v3 import InceptionV3
from tensorflow.contrib.keras.python.keras.preprocessing import image
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import preprocess_input, decode_predictions
import numpy as np
import time

img_path = 'p1m.jpg' #0.png 1.png
img = image.load_img(img_path, target_size = (299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x = preprocess_input(x)
begin = time.clock()
model = InceptionV3(weights = 'imagenet')
preds = model.predict(x)
print ('Predicted:', decode_predictions(preds, top = 3)[0])
print ('Time elapsed: %.0f' % (time.clock() - begin))

VGG16の場合

Resource exhausted: OOM when allocating tensor with shape[25088,4096]
強制終了

サンプルコード

from tensorflow.contrib.keras.python.keras.applications.vgg16 import VGG16
from tensorflow.contrib.keras.python.keras.preprocessing import image
from tensorflow.contrib.keras.python.keras.applications.imagenet_utils import preprocess_input, decode_predictions
import numpy as np
import time

img_path = 'p1m.jpg' #0.png 1.png
img = image.load_img(img_path, target_size = (224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x = preprocess_input(x)
begin = time.clock()
model = VGG16(weights = 'imagenet')
preds = model.predict(x)
print ('Predicted:', decode_predictions(preds, top = 3)[0])
print ('Time elapsed: %.0f' % (time.clock() - begin))

以上。

0
Help us understand the problem. What is going on with this article?
Why not register and get more from Qiita?
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away
0
Help us understand the problem. What is going on with this article?