yolo_v1(pretrained: bool = False, overrides: object = {})YOLOv1 with full Darknet backbone (Redmon et al., CVPR 2016).
Builds the paper-cited full YOLOv1 detector: 24-layer Darknet backbone, 7x7 grid, 2 boxes per cell, 20 PASCAL VOC classes (or 80 COCO classes if overridden). Approximately 270M parameters (most in the two 4096-dim FC layers) and 63.4% mAP on VOC 2007 test (paper Table 1) at 45 fps.
Model Size
Parameters
pretrainedbool= FalseReserved for future pretrained-weight loading. Currently ignored.
**overridesobject= {}Keyword overrides forwarded into
YOLOV1Config —
num_classes, split_size (S), num_boxes (B),
score_thresh, nms_thresh, and loss weights.Returns
YOLOV1ForObjectDetectionDetector with the full YOLOv1 configuration applied (or with
overrides merged on top of it).
Notes
See Redmon et al., "You Only Look Once: Unified, Real-Time Object Detection", CVPR 2016 (arXiv:1506.02640).
Examples
>>> import lucid
>>> from lucid.models.vision.yolo._v1 import yolo_v1
>>> model = yolo_v1()
>>> x = lucid.randn(1, 3, 448, 448)
>>> out = model(x)
>>> out.logits.shape[0]
1