Object Detection:

• YOLO (You Only Look Once): Using the YOLO model for object detection in images. YOLO is a real-time object detection model that predicts bounding boxes and class probabilities directly from full images in one evaluation.
• RetinaNet for Object Detection: Keras also has an example for RetinaNet, an object detection model that uses a focal loss function to address class imbalance during training.

pythonCopy codefrom tensorflow.keras.applications import ResNet50
from tensorflow.keras import layers, models

# Load pre-trained ResNet50 model and use as a backbone for RetinaNet
backbone = ResNet50(include_top=False, input_shape=(224, 224, 3))

# Define custom RetinaNet layers on top of the backbone
model = models.Sequential([
    backbone,
    layers.Conv2D(256, 3, activation='relu'),
    layers.Conv2D(256, 3, activation='relu'),
    layers.Conv2D(9 * 4, 1)  # For bounding box predictions
])

model.compile(optimizer='adam', loss='categorical_crossentropy')