YOLOV8 backbones

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YOLOV8Backbone class

keras_cv.models.YOLOV8Backbone(
    stackwise_channels,
    stackwise_depth,
    include_rescaling,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Implements the YOLOV8 backbone for object detection.

This backbone is a variant of the CSPDarkNetBackbone architecture.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

• stackwise_channels: A list of ints, the number of channels for each dark level in the model.
• stackwise_depth: A list of ints, the depth for each dark level in the model.
• include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
• activation: String. The activation functions to use in the backbone to use in the CSPDarkNet blocks. Defaults to "swish".
• input_shape: optional shape tuple, defaults to (None, None, 3).
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

Returns

A keras.Model instance.

Examples

input_data = tf.ones(shape=(8, 224, 224, 3))

# Pretrained backbone
model = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_xs_backbone_coco"
)
output = model(input_data)

# Randomly initialized backbone with a custom config
model = keras_cv.models.YOLOV8Backbone(
    stackwise_channels=[128, 256, 512, 1024],
    stackwise_depth=[3, 9, 9, 3],
    include_rescaling=False,
)
output = model(input_data)

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from_preset method

YOLOV8Backbone.from_preset()

Instantiate YOLOV8Backbone model from preset config and weights.

Arguments

• preset: string. Must be one of "yolo_v8_xs_backbone", "yolo_v8_s_backbone", "yolo_v8_m_backbone", "yolo_v8_l_backbone", "yolo_v8_xl_backbone", "yolo_v8_xs_backbone_coco", "yolo_v8_s_backbone_coco", "yolo_v8_m_backbone_coco", "yolo_v8_l_backbone_coco", "yolo_v8_xl_backbone_coco". If looking for a preset with pretrained weights, choose one of "yolo_v8_xs_backbone_coco", "yolo_v8_s_backbone_coco", "yolo_v8_m_backbone_coco", "yolo_v8_l_backbone_coco", "yolo_v8_xl_backbone_coco".
• load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.

Examples

# Load architecture and weights from preset
model = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_xs_backbone_coco",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_xs_backbone_coco",
    load_weights=False,