Crax Rat !link! May 2026

# Assuming you've collected and preprocessed your data train_dir = 'path/to/train' validation_dir = 'path/to/validation'

# Continue training This example demonstrates how to use transfer learning with VGG16 for a binary classification task. Adapt it according to your dataset and objectives. The approach to preparing deep features for Crax rubra or any wildlife species involves thoughtful data collection, preprocessing, and model selection. Leveraging pre-trained models through transfer learning can significantly improve performance, especially when dealing with limited datasets. crax rat

# Training history = model.fit(train_generator, steps_per_epoch=train_generator.samples // 32, validation_data=validation_generator, validation_steps=validation_generator.samples // 32, epochs=10) # Assuming you've collected and preprocessed your data

# Freeze base layers for layer in base_model.layers: layer.trainable = False steps_per_epoch=train_generator.samples // 32

# Data augmentation train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)

# Building the model base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))