Licensing

LibreYOLO's own code

The library is MIT. That covers the Python API, the CLI, the trainers, validators and exporters, the dataset loaders, and the conversion scripts under weights/. Use it in a commercial or closed-source product, keep the copyright line and the license text with any copy you redistribute, and the obligation ends there.

The grant stops at the code. The LICENSE file puts it plainly:

Those licenses vary and are not all permissive: some published weights are non-commercial or otherwise restricted, and this MIT License does not extend to them. Choosing a model means choosing its license.

Upstream code, per family

Most families are ports of published research, and several vendor upstream source directly. A vendored file keeps its original copyright header and its original license. MIT does not overwrite it, and LibreYOLO does not relicense anyone's work. Apache-2.0 and BSD-3-Clause are the two that come up most often.

Apache-2.0 covers the DETR line and much of the transformer work: DETR from Meta AI (FAIR), Deformable DETR from SenseTime, LW-DETR from Baidu, OV-DEIM by Leilei Wang and coauthors, the SegFormer implementation LibreYOLO ports from Hugging Face Transformers, PP-OCRv5 from the PaddlePaddle Authors, SwinIR from the Computer Vision Lab at ETH Zurich, and Depth Anything 3 from ByteDance Seed. It also covers the classifiers derived from timm by Ross Wightman and the timm contributors, among them ResNet, DeiT, EfficientNetV2, MobileNetV4 and Swin, whose module names mirror timm so that its ImageNet tensors load unchanged.

BSD-3-Clause covers everything derived from torchvision: Faster R-CNN, Mask R-CNN, FCOS, RetinaNet, SSD300, AlexNet, VGG, FCN and DeepLabv3.

MIT covers a smaller group, including NAFNet from Megvii, CenterNet from Xingyi Zhou, and YOLOv7 as re-released by its own authors, Kin-Yiu Wong and Hao-Tang Tsui, at MultimediaTechLab. The YOLOv1 through YOLOv4 families reproduce architectures from the Darknet project, by Joseph Redmon and, for YOLOv4, by Alexey Bochkovskiy. Darknet is public domain, so those carry no obligation at all.

One bundled subtree is not an open-source license. The DEIMv2 family ships DINOv3 backbone code from Meta Platforms under the DINOv3 License Agreement, a custom non-OSI license. Redistributing that code means shipping a copy of the agreement with it, and the agreement forbids use for activities subject to ITAR, military or warfare purposes, nuclear industries, espionage, and weapons development. Those terms bind that subtree only.

Two files in the repository hold the full picture. NOTICE lists every bundled third-party subtree with its path, its license file and its upstream source. THIRD_PARTY_NOTICES.txt lists the upstream projects LibreYOLO derives from and reproduces each license text in full.

Weights, per checkpoint

No pretrained weight file ships inside the package. Published checkpoints live on Hugging Face under the LibreYOLO organization, and each repository carries its own LICENSE and attribution reflecting the project the weights came from.

That repository is the authoritative source for the terms. Not this page, not the model page, and not the summary in the source tree. See checkpoints and weights for how files are named and where they are downloaded from.

Licenses differ between families, and they differ between files inside one family. Two examples of the second:

• The YOLO9 COCO checkpoints are MIT. LibreYOLO9P2s-visdrone.pt, trained on VisDrone2019-DET, is CC BY-NC-SA 3.0, which is non-commercial.
• The RF-DETR detection checkpoints are Apache-2.0. The oriented-box checkpoints are CC BY 4.0, because they were fine-tuned on a Roboflow Universe dataset published under CC BY 4.0 and the weights carry that dataset's attribution requirement forward.

Across families, the range runs wider, and several published checkpoints cannot be used in a commercial product:

• SegFormer is the clearest split between the two layers. The implementation is an Apache-2.0 port of Hugging Face Transformers' code. The published ADE20K checkpoints are converted from NVIDIA's release under the NVIDIA Source Code License, which permits redistribution but limits use to non-commercial research or evaluation, and carries that limit forward into derivative works. Those checkpoints are not covered by LibreYOLO's permissive terms.
• OV-DEIM checkpoints are CC BY-NC 4.0, confirmed by the upstream author. Every prediction also loads Apple's MobileCLIP-B(LT) text tower, whose license restricts use to research, a stricter term than the checkpoint's own.
• SenseNova-Vision code is Apache-2.0 and its weights are CC BY-NC 4.0. The loader prints the non-commercial notice before every automatic download.

Some families have no checkpoint hosted by LibreYOLO at all, and their pages say so in the Weights row. SAM 3 is gated on Hugging Face under Meta's custom SAM License and is downloaded from Meta directly. MiDaS release assets are fetched from the official URLs and hash-verified rather than rehosted. Dome-DETR is linked upstream because its model card states no license in its metadata while its prose claims Apache-2.0 and restricts use to academic research at the same time, and those do not agree. The TEED and DexiNed architectures are MIT, but the authors' released checkpoints were trained on BIPED, whose dataset terms are non-commercial, so LibreYOLO neither bundles nor auto-downloads them.

Several torchvision checkpoints carry no license file of their own. LibreYOLO mirrors them on the license the releasing project uses, states on each model card that the basis is implied rather than granted per checkpoint, and repeats torchvision's own warning that pretrained model terms may derive from the training data.

Finding the terms for one model

The model page carries a Licenses row in its header, in the form Code X, weights Y, which links down to the page's Licensing section. That section lists the original work and its authors, the upstream license, the upstream source, the LibreYOLO code license, the weights, and an interpretation of what the terms allow. The Checkpoints table on the same page has a Weights license column, one row per published file, so a family with mixed terms shows them file by file.

All of that renders from the same data the library is checked against, which is why this page does not repeat it as a table. A hand-typed license matrix is wrong within one release, and wrong here is expensive.

In the source tree, the equivalents are NOTICE for bundled code, THIRD_PARTY_NOTICES.txt for upstream projects and their license texts, and weights/LICENSE_NOTICE.txt for a per-family summary of the published checkpoints.

Then check the Hugging Face repository of the exact file you are about to download. It is authoritative, and it can change without a docs page changing with it.

Commercial use

Code is rarely the problem. MIT, Apache-2.0 and BSD-3-Clause all permit commercial and closed-source use. Each asks you to keep its license text and attribution notices with copies you redistribute, Apache-2.0 also grants a patent license, and none of them places conditions on your own application code.

Checkpoints are where products get stuck. A non-commercial checkpoint stays non-commercial however permissive the surrounding code is, and converting the file does not change its applicable terms, which is what weights/LICENSE_NOTICE.txt states directly. An ONNX or TensorRT artifact built from a restricted checkpoint inherits the restriction.

Where a license carries its restriction into derivative works, as the NVIDIA Source Code License does, fine-tuning does not escape it either. Training the same architecture from scratch on data you have the right to use does: the code is permissive, so a model you train yourself is yours, and the pretrained checkpoint's terms never enter it. The SegFormer page spells that out for its own weights; read the Interpretation row on the page of whichever family you plan to ship.

Decide the license question when you pick the model rather than when you ship, and read the terms on the file you actually downloaded, because a family with one permissive checkpoint can have a restricted one beside it.

Not legal advice

This page describes the licenses involved. It is a description, not legal advice, and it does not create any warranty. If the answer matters commercially, read the licenses yourself and take your own counsel.