LibreYOLO
Copyright (c) The LibreYOLO contributors.

LibreYOLO is licensed under the MIT License (see LICENSE).

This product bundles third-party source code under non-MIT licenses. The
bundled files retain their original copyright headers. A copy of each
upstream license accompanies the corresponding code.

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Bundled third-party source code
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DINOv3 (Meta DINOv3 License Agreement)
    Path:    libreyolo/models/deimv2/engine/backbone/dinov3/
    License: libreyolo/models/deimv2/engine/backbone/dinov3/LICENSE.md
    Source:  https://github.com/facebookresearch/dinov3

    The DINOv3 backbone code shipped with the DEIMv2 family is
    redistributed under the DINOv3 License Agreement, a custom
    non-OSI license from Meta Platforms, Inc.

    Key obligations propagated to downstream users (see LICENSE.md
    for the authoritative text):
      * Redistribution must include a copy of the DINOv3 License
        Agreement (provided as LICENSE.md alongside the code).
      * The DINO Materials may not be used for activities subject
        to ITAR, military or warfare purposes, nuclear industries,
        espionage, or weapons development.
      * The DINO Materials are provided "as is" without warranty.

    These terms apply only to the DINOv3 subtree listed above. The
    rest of LibreYOLO remains under the MIT License.

MobileNetV4 / timm (Apache License 2.0)
    Path:    libreyolo/models/mobilenetv4/
    License: libreyolo/models/mobilenetv4/NOTICE  (Apache-2.0)
    Source:  https://github.com/huggingface/pytorch-image-models

    The MobileNetV4 architecture (libreyolo/models/mobilenetv4/nn.py) is a
    native re-implementation derived from timm's MobileNetV4
    (Ross Wightman and the timm contributors), licensed Apache-2.0. Module
    naming mirrors timm so its Apache-2.0 ImageNet-1k pretrained weights load
    unchanged and inference is bit-identical. Apache-2.0 is MIT-compatible;
    these terms add only attribution obligations.

ConvNeXt / timm (MIT code + Apache-2.0 weights)
    Path:    libreyolo/models/convnext/
    License: libreyolo/models/convnext/NOTICE
    Source:  https://github.com/facebookresearch/ConvNeXt
             https://github.com/huggingface/pytorch-image-models

    The ConvNeXt V1 architecture (libreyolo/models/convnext/nn.py) is a native
    re-implementation derived from Meta's ConvNeXt (MIT) and timm's ConvNeXt.
    Module naming mirrors timm so its Apache-2.0 ImageNet-1k `fb_in1k` weights
    load unchanged and inference is bit-identical. ConvNeXt-V2's small
    checkpoints are CC-BY-NC and are intentionally NOT used.

EfficientNetV2 / timm (Apache License 2.0)
    Path:    libreyolo/models/efficientnetv2/
    License: libreyolo/models/efficientnetv2/NOTICE  (Apache-2.0)
    Source:  https://github.com/huggingface/pytorch-image-models
             https://github.com/google/automl

    The EfficientNetV2 architecture (libreyolo/models/efficientnetv2/nn.py) is a
    native re-implementation derived from timm's EfficientNetV2 (Ross Wightman)
    and Google's EfficientNetV2 (google/automl), licensed Apache-2.0. Module
    naming mirrors timm so its Apache-2.0 ImageNet-1k weights load unchanged and
    inference is bit-identical.

ResNet / timm (Apache License 2.0)
    Path:    libreyolo/models/resnet/
    License: libreyolo/models/resnet/NOTICE  (Apache-2.0)
    Source:  https://github.com/huggingface/pytorch-image-models

    The ResNet architecture (libreyolo/models/resnet/nn.py) is a native
    re-implementation of the vanilla ResNet (He et al. 2015, v1.5). Weights are
    timm's `resnet*.a1_in1k` (Ross Wightman), Apache-2.0, ImageNet-1k. Module
    naming mirrors timm/torchvision so weights load unchanged and inference is
    bit-identical.

NAFNet (MIT License)
    Path:    libreyolo/models/nafnet/
    License: libreyolo/models/nafnet/NOTICE  (MIT)
    Source:  https://github.com/megvii-research/NAFNet

    The NAFNet restoration architecture (libreyolo/models/nafnet/nn.py) is a
    native PyTorch implementation derived from Megvii Research's NAFNet
    (MIT). LibreYOLO does not bundle NAFNet pretrained checkpoint files. Some
    published GoPro-trained NAFNet weights do not carry an explicit standalone
    weights license; convert only weights that you have the right to use and
    redistribute.

Darknet YOLOv2 / YOLOv3 / YOLOv4 (public domain)
    Path:    libreyolo/models/darknet/, libreyolo/models/yolo2|yolo3|yolo4/
    License: libreyolo/models/darknet/cfgs/NOTICE  (public domain)
    Source:  https://github.com/pjreddie/darknet  (YOLOv2/v3)
             https://github.com/AlexeyAB/darknet   (YOLOv4)

    The LibreYOLO2/3/4 families reproduce the YOLOv2, YOLOv3, and YOLOv4
    architectures from the Darknet project, which is public domain (the
    "YOLO LICENSE": "Darknet is public domain. Do whatever you want with
    it."). The model-definition (.cfg) files are bundled under
    libreyolo/models/darknet/cfgs/ and drive both the runtime graph builder
    and the weight converter. Only the .cfg format and the numerical
    behaviour of the Darknet layers are reproduced; no Darknet C source is
    copied. Being public domain, these terms impose no obligations on
    LibreYOLO or its downstream users.

YOLOv7 / MultimediaTechLab/YOLO (MIT License)
    Path:    libreyolo/models/yolo7/
    Source:  https://github.com/MultimediaTechLab/YOLO

    The LibreYOLO7 family is a native port of YOLOv7 from
    MultimediaTechLab/YOLO (MIT, (c) 2024 Kin-Yiu Wong & Hao-Tang Tsui) —
    the authors' own MIT re-release, NOT the GPL-3.0 WongKinYiu/yolov7.
    Module names mirror upstream so the MIT v7.pt weights load unchanged;
    the model-definition v7.yaml is bundled under libreyolo/models/yolo7/.
    MIT is MIT-compatible; these terms add only attribution obligations.

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Pretrained model weights
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No pretrained weights are distributed in this source tree. LibreYOLO
weights are published separately on Hugging Face under the LibreYOLO
organization (https://huggingface.co/LibreYOLO). Each Hugging Face
model repository ships its own LICENSE and NOTICE reflecting the
license of the upstream project the weights were derived from. See
weights/LICENSE_NOTICE.txt for a per-family summary.
