tensorflow_image_models-0.0.11.tar.gz

Install<br /> pip install tensorflow_image_models==0.0.11 ---------------------------------------------- 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 /> .....<br /> .....<br /> ViTGaint_14<br /> ViTGaint_16<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() ---------------------------------------------- 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 /> ViTTiny = (224, 224, 3)<br /> ViTSmall_8 = (224, 224, 3)<br /> ViTSmall_16 = (224, 224, 3)<br /> ViTSmall_32 = (224, 224, 3)<br /> ViTBase_8 = (224, 224, 3)<br /> ViTBase_16 = (224, 224, 3)<br /> ViTBase_32 = (224, 224, 3)<br /> ViTLarge_14 = (224, 224, 3)<br /> ViTLarge_16 = (224, 224, 3)<br /> ViTLarge_32 = (224, 224, 3)<br /> ViTHuge_14 = (224, 224, 3)<br /> ViTHuge_16 = (224, 224, 3)<br /> ViTGaint_14 = (224, 224, 3)<br /> ViTGaint_16 = (224, 224, 3)<br /> The rest of the models receive any shape, and the default shape is (128,128,3) ---------------------------------------------- License<br /> This project is licensed under the MIT License ---------------------------------------------- Author<br /> DEEPOLOGY LAB