Metadata-Version: 2.1
Name: tensorflow_image_models
Version: 0.0.10
Summary: Tensorflow image models
Author: DEEPOLOGY LAB
Author-email: alaa.m.elmor@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: license.txt

Install<br />

pip install tensorflow_image_models==0.0.10

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Usage<br />

from tensorflow_image_models import list_models<br />

list_models()<br />

models list:<br />
DPN92<br />
DPN98<br />
DPN131<br />
DPN107<br />
EfficientNetB0<br />
EfficientNetB1<br />
.....<br />
.....<br />
VGG16<br />
VGG19<br />

Create Model<br />

from tensorflow_image_models import EfficientNet<br />
model = EfficientNet.EfficientNetB0(classes=10)<br />
model.summary()


from tensorflow_image_models import Inception<br />
model = Inception.InceptionV4(classes=3, input_shape=(299,299,3)<br />
model.summary()

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Here is a list of input shape expected for each model

EfficientNetB0 = (224, 224, 3)<br />
EfficientNetB1 = (240, 240, 3)<br />
EfficientNetB2 = (260, 260, 3)<br />
EfficientNetB3 = (300, 300, 3)<br />
EfficientNetB4 = (380, 380, 3)<br />
EfficientNetB5 = (456, 456, 3)<br />
EfficientNetB6 = (528, 528, 3)<br />
EfficientNetB7 = (600, 600, 3)<br />
AlexNet = (224, 224, 3)<br />
InceptionResNetV2 = (299, 299, 3)<br />
InceptionV3 = (299, 299, 3)<br />
LeNet = (32,  32,  1)<br />
MobileNetV2 = (224, 224, 3)<br />
ResNet18 = (224, 224, 3)<br />
ResNet34 = (224, 224, 3)<br />
VoVNet27 = (224, 224, 3)<br />
VoVNet39 = (224, 224, 3)<br />
VoVNet57 = (224, 224, 3)<br />


The rest of the models receive any shape, and the default shape is (128,128,3)

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License<br />
This project is licensed under the MIT License

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Author<br />
DEEPOLOGY LAB
