Computer Vision News - May 2023

10 Computer Vision Code # Add classification head x = layers.GlobalAveragePooling2D()(resnet.output) x = layers.Dense(256, activation='relu')(x) x = layers.Dropout(0.5)(x) x = layers.Dense(4, activation='softmax')(x) # Build the model model = models.Model(inputs=resnet.input, outputs=x) # Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Load blood cell images and train the model # See the reference Kaggle In this example, we load a pre-trained ResNet model and add a classification head to the top of the network. The classification head consists of a global average pooling layer, followed by two fully connected layers with dropout and a softmax activation function for multi-class classification. During training, we can use a categorical cross-entropy loss function to optimize the model. Image 2: Leykocyte detection: An application of Mono- vs Polymorpho- nuclear from Cheuque, C.; Querales, M.; León, R.; Salas, R.; Torres, R. An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification. Diagnostics 2022 , 12, 248. https://doi.org/10.3390/diagnostics12020248