lucid-dl/yolo-v2

YOLOv2 (Darknet-19)

Redmon & Farhadi, 2017 — YOLO9000: Better, Faster, Stronger (arXiv:1612.08242)

Lucid port of darknet/yolov2.weights, converted to Lucid-native safetensors.

Available weights

Tag	mAP@0.5	Params	GFLOPs	Size	Source
COCO_2014 (default)	48.1	51.0M	—	194.5 MB	darknet

Usage

import lucid.models as models
from lucid.models.weights import YOLOV2Weights

# default tag
model = models.yolo_v2(pretrained=True)

# explicit tag (enum or string)
model = models.yolo_v2(weights=YOLOV2Weights.COCO_2014)
model = models.yolo_v2(pretrained="COCO_2014")

# preprocessing travels with the weights
weights = YOLOV2Weights.COCO_2014
preprocess = weights.transforms()
out = model(preprocess(image)[None])
# ObjectDetectionOutput: per-query/proposal class logits + boxes
logits, boxes = out.logits, out.pred_boxes

Conversion

Converted from darknet/yolov2.weights via python -m tools.convert_weights yolo_v2 --tag COCO_2014. Key mapping + numerical parity verified against the source.

License

other — inherited from the original weights.

Citation

@inproceedings{redmon2017yolo9000,
  title={YOLO9000: Better, Faster, Stronger},
  author={Redmon, Joseph and Farhadi, Ali},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}