Downloads · 30 days
0
LibreYOLO/LibreDOMEDETRm-visdrone
LibreDOMEDETRm-visdrone is a object detection model from LibreYOLO. Use it when you need objects located in an image. It is set up for libreyolo. The card lists the license as other.
Dome-DETR M trained on VisDrone (12 output classes), converted for LibreYOLO at 800x800.
Downloads · 30 days
0
Access
Public
Updated Aug 15, 2026
Repo size
98 MB
Likes
0
Public
Click a slice to open those files.
.pt98 MB · 100%
From the Hugging Face model README
Dome-DETR M trained on VisDrone (12 output classes), converted for LibreYOLO at 800x800.
ACADEMIC-RESEARCH-ONLY WEIGHTS
The upstream model card says these weight files are for academic research purposes only. They are not covered by LibreYOLO's MIT license and must not be treated as commercially cleared.
The same upstream card also says the project is Apache-2.0, but it has no license metadata and links to a LICENSE file that does not exist in the weight repository. LibreYOLO's maintainer approved this mirror by treating the Apache statement as the redistribution basis and preserving the stricter academic-only sentence as the use restriction. This is LibreYOLO's disclosed interpretation, not clarification from the Dome-DETR authors. Review the pinned upstream card and the
LICENSEandNOTICEin this repository before use.
from libreyolo import LibreYOLO
model = LibreYOLO("LibreDOMEDETRm-visdrone.pt")
results = model.predict("image.jpg")
Official upstream weight file:
best_ckpts_dome_2026/dome-m-visdrone_converted.pth
530230620d1f3261a267d462989cddf204cc6e103a0d1db3c68fac5239e1b43b4c6f765b1330e6961070113f4ea39dbdbf8846a6fbbde98bbca0c80b23162c654de40f8a387ccacfd98bf0bdd16d5d941cbc1b64The Dome-DETR architecture source is Apache-2.0 at commit
2dde3bc1946a3e9fad9abd0612b59fc39bd6b861.
Copyright (c) 2025 The Dome-DETR Authors, as stated in its source headers.
That source-code license and LibreYOLO's MIT code license do not remove the
academic-only restriction stated for these pretrained weights.
LibreYOLO adds checkpoint-schema metadata, including the dataset variant,
class names, pinned source revision, and source SHA-256. State-dict keys and
learned tensors are unchanged. The converted checkpoint loads strictly into
LibreYOLO's native implementation. See
weights/convert_domedetr_weights.py.
Academic research purposes only, following the stricter statement on the
upstream weight card. The upstream Apache-2.0 statement and its missing weight
repository LICENSE file are documented without omission. See
LICENSE and NOTICE.