Computer Vision News - May 2023

12 Computer Vision Code Expanded 3D Densenet example: import numpy as np from keras.models import Model from keras.layers import Input, Conv3D, Dense, Dropout, GlobalAveragePooling3D from keras.applications.densenet import DenseNet121 from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from sklearn.model_selection import train_test_split def build_3d_densenet(input_shape, n_classes): inputs = Input(shape=input_shape) # Load pretrained Densenet model base_model = DenseNet121(input_shape=input_shape, include_top=False) # Add 3D convolutional layers x = Conv3D(64, (3, 3, 3), padding='same')(inputs) x = base_model(x) x = Conv3D(64, (3, 3, 3), padding='same')(x) x = GlobalAveragePooling3D()(x) x = Dropout(0.5)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) outputs = Dense(n_classes, activation='softmax')(x) # Build the model model = Model(inputs, outputs) return model # Load your dataset (X and y) # X: (num_samples, depth, height, width, channels) # y: (num_samples, n_classes) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create the 3D Densenet model input_shape = (224, 224, 224, 3) # Replace with your input shape n_classes = 2 # Replace with your number of classes model = build_3d_densenet(input_shape, n_classes) # Compile the model