ResNet and ResNetV2 - Keras
For ResNet, call keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.
ResNet - Keras
Keras documentation. ResNetBackbone model. ResNetImageClassifier model
InceptionResNetV2 - Keras
InceptionResNetV2 (include_top = True, weights = "imagenet", input_tensor = None, input_shape = None, pooling = None, classes = 1000, classifier_activation = "softmax", name = "inception_resnet_v2",) Instantiates the Inception-ResNet v2 architecture.
ResNet and ResNetV2 - Keras
For ResNetV2, call tf.keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.preprocess_input will scale input pixels between -1 and 1. Arguments
ResNetBackbone model - Keras
50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.
Keras Applications
About Keras Getting started Developer guides Code examples Keras 3 API documentation Models API Layers API Callbacks API Ops API Optimizers Metrics Losses Data loading Built-in small datasets Keras Applications Xception EfficientNet B0 to B7 EfficientNetV2 B0 to B3 and S, M, L ConvNeXt Tiny, Small, Base, Large, XLarge VGG16 and VGG19 ResNet and ...
DeepLabV3Backbone model - Keras
A somewhat sensible backbone to use in many cases is the keras_hub.models.ResNetBackbone.from_preset("resnet_v2_50"). projection_filters : int. Number of filters in the convolution layer projecting low-level features from the image_encoder .
ResNetImageClassifierPreprocessor layer - Keras
50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.
Keras documentation: KerasHub pretrained models
50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.
ImageClassifierPreprocessor - Keras
50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.