The YOLOV8Detector model

[source]

YOLOV8Detector class

keras_cv.models.YOLOV8Detector(
    backbone,
    num_classes,
    bounding_box_format,
    fpn_depth=2,
    label_encoder=None,
    prediction_decoder=None,
    **kwargs
)

Implements the YOLOV8 architecture for object detection.

Arguments

• backbone: keras.Model, must implement the pyramid_level_inputs property with keys "P2", "P3", and "P4" and layer names as values. A sensible backbone to use is the keras_cv.models.YOLOV8Backbone.
• num_classes: integer, the number of classes in your dataset excluding the background class. Classes should be represented by integers in the range [0, num_classes).
• bounding_box_format: string, the format of bounding boxes of input dataset. Refer to the keras.io docs for more details on supported bounding box formats.
• fpn_depth: integer, a specification of the depth of the CSP blocks in the Feature Pyramid Network. This is usually 1, 2, or 3, depending on the size of your YOLOV8Detector model. We recommend using 3 for "yolo_v8_l_backbone" and "yolo_v8_xl_backbone". Defaults to 2.
• label_encoder: (Optional) A YOLOV8LabelEncoder that is responsible for transforming input boxes into trainable labels for YOLOV8Detector. If not provided, a default is provided.
• prediction_decoder: (Optional) A keras.layers.Layer that is responsible for transforming YOLOV8 predictions into usable bounding boxes. If not provided, a default is provided. The default prediction_decoder layer is a keras_cv.layers.MultiClassNonMaxSuppression layer, which uses a Non-Max Suppression for box pruning.

Examples

images = tf.ones(shape=(1, 512, 512, 3))
labels = {
    "boxes": tf.constant([
        [
            [0, 0, 100, 100],
            [100, 100, 200, 200],
            [300, 300, 100, 100],
        ]
    ], dtype=tf.float32),
    "classes": tf.constant([[1, 1, 1]], dtype=tf.int64),
}

model = keras_cv.models.YOLOV8Detector(
    num_classes=20,
    bounding_box_format="xywh",
    backbone=keras_cv.models.YOLOV8Backbone.from_preset(
        "yolo_v8_m_backbone_coco"
    ),
    fpn_depth=2
)

# Evaluate model without box decoding and NMS
model(images)

# Prediction with box decoding and NMS
model.predict(images)

# Train model
model.compile(
    classification_loss='binary_crossentropy',
    box_loss='ciou',
    optimizer=tf.optimizers.SGD(global_clipnorm=10.0),
    jit_compile=False,
)
model.fit(images, labels)

[source]

from_preset method

YOLOV8Detector.from_preset()

Instantiate YOLOV8Detector model from preset config and weights.

Arguments

• preset: string. Must be one of "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet18_v2", "resnet34_v2", "resnet50_v2", "resnet101_v2", "resnet152_v2", "mobilenet_v3_small", "mobilenet_v3_large", "csp_darknet_tiny", "csp_darknet_s", "csp_darknet_m", "csp_darknet_l", "csp_darknet_xl", "efficientnetv1_b0", "efficientnetv1_b1", "efficientnetv1_b2", "efficientnetv1_b3", "efficientnetv1_b4", "efficientnetv1_b5", "efficientnetv1_b6", "efficientnetv1_b7", "efficientnetv2_s", "efficientnetv2_m", "efficientnetv2_l", "efficientnetv2_b0", "efficientnetv2_b1", "efficientnetv2_b2", "efficientnetv2_b3", "densenet121", "densenet169", "densenet201", "efficientnetlite_b0", "efficientnetlite_b1", "efficientnetlite_b2", "efficientnetlite_b3", "efficientnetlite_b4", "yolo_v8_xs_backbone", "yolo_v8_s_backbone", "yolo_v8_m_backbone", "yolo_v8_l_backbone", "yolo_v8_xl_backbone", "vitdet_base", "vitdet_large", "vitdet_huge", "resnet50_imagenet", "resnet50_v2_imagenet", "mobilenet_v3_large_imagenet", "mobilenet_v3_small_imagenet", "csp_darknet_tiny_imagenet", "csp_darknet_l_imagenet", "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet", "densenet121_imagenet", "densenet169_imagenet", "densenet201_imagenet", "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", "vitdet_base_sa1b", "vitdet_large_sa1b", "vitdet_huge_sa1b", "yolo_v8_m_pascalvoc". If looking for a preset with pretrained weights, choose one of "resnet50_imagenet", "resnet50_v2_imagenet", "mobilenet_v3_large_imagenet", "mobilenet_v3_small_imagenet", "csp_darknet_tiny_imagenet", "csp_darknet_l_imagenet", "efficientnetv2_s_imagenet", "efficientnetv2_b0_imagenet", "efficientnetv2_b1_imagenet", "efficientnetv2_b2_imagenet", "densenet121_imagenet", "densenet169_imagenet", "densenet201_imagenet", "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", "vitdet_base_sa1b", "vitdet_large_sa1b", "vitdet_huge_sa1b", "yolo_v8_m_pascalvoc".
• load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.
• input_shape : input shape that will be passed to backbone initialization, Defaults to None.If None, the preset value will be used.

Examples

# Load architecture and weights from preset
model = keras_cv.models.YOLOV8Detector.from_preset(
    "resnet50_imagenet",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.YOLOV8Detector.from_preset(
    "resnet50_imagenet",
    load_weights=False,