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lucid-dl/yolo-v3-tiny
yolo-v3-tiny is a object detection model from lucid-dl. Use it when you need objects located in an image. It is set up for lucid. The card lists the license as other.
Redmon & Farhadi, 2018 — YOLOv3: An Incremental Improvement (arXiv:1804.02767)
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Updated Aug 19, 2026
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From the Hugging Face model README
Redmon & Farhadi, 2018 — YOLOv3: An Incremental Improvement (arXiv:1804.02767)
Lucid port of darknet/yolov3-tiny.weights,
converted to Lucid-native safetensors.
| Tag | [email protected] | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|
COCO_2014 (default) | 33.1 | 8.9M | — | 33.8 MB | darknet |
import lucid.models as models
from lucid.models.weights import YOLOV3TinyWeights
# default tag
model = models.yolo_v3_tiny(pretrained=True)
# explicit tag (enum or string)
model = models.yolo_v3_tiny(weights=YOLOV3TinyWeights.COCO_2014)
model = models.yolo_v3_tiny(pretrained="COCO_2014")
# preprocessing travels with the weights
weights = YOLOV3TinyWeights.COCO_2014
preprocess = weights.transforms()
out = model(preprocess(image)[None])
# ObjectDetectionOutput: per-query/proposal class logits + boxes
logits, boxes = out.logits, out.pred_boxes
Converted from darknet/yolov3-tiny.weights via
python -m tools.convert_weights yolo_v3_tiny --tag COCO_2014.
Key mapping + numerical parity verified against the source.
other — inherited from the original weights.
@article{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal={arXiv preprint arXiv:1804.02767},
year={2018}
}