YOLO_V1Config¶

class lucid.models.YOLO_V1Config(in_channels: int = 3, split_size: int = 7, num_boxes: int = 2, num_classes: int = 20, lambda_coord: float = 5.0, lambda_noobj: float = 0.5, conv_config: list[object] = <factory>)¶

YOLO_V1Config stores the input channel count, detection grid size, number of boxes and classes, loss weights, and convolutional backbone layout used by lucid.models.YOLO_V1.

Class Signature¶

@dataclass
class YOLO_V1Config:
    in_channels: int = 3
    split_size: int = 7
    num_boxes: int = 2
    num_classes: int = 20
    lambda_coord: float = 5.0
    lambda_noobj: float = 0.5
    conv_config: list[object] = field(default_factory=...)

Parameters¶

in_channels (int): Number of input image channels consumed by the backbone.
split_size (int): Detection grid size S used by the final prediction head.
num_boxes (int): Number of bounding boxes predicted per grid cell.
num_classes (int): Number of class logits predicted per grid cell.
lambda_coord, lambda_noobj (float): Loss weights for box-coordinate and no-object confidence terms.
conv_config: YOLO-v1 convolutional architecture definition using tuple, “M”, and repeated-block entries.

Validation¶

in_channels, split_size, num_boxes, and num_classes must be greater than 0.
lambda_coord and lambda_noobj must be non-negative.
conv_config must be a non-empty list using valid YOLO-v1 architecture entries.
conv_config must end with a 1024-channel convolution because the fully connected head expects that feature width.

Usage¶

import lucid.models as models

config = models.YOLO_V1Config(
    split_size=2,
    num_boxes=2,
    num_classes=3,
    conv_config=[(1024, 1, 1, 0)],
)
model = models.YOLO_V1(config)