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.

SenseNova-Vision / Bagel (Apache License 2.0 code; CC BY-NC 4.0 weights,
not redistributed)
    Path:    libreyolo/models/sensenova/
    License: libreyolo/models/sensenova/NOTICE  (Apache-2.0 code sources)
    Source:  https://github.com/OpenSenseNova/SenseNova-Vision

    The SenseNova-Vision family vendors an inference-only port of the
    upstream Apache-2.0 implementation (SenseTime 2026, building on
    ByteDance's Bagel, Hugging Face transformers, and the Black Forest
    Labs FLUX autoencoder, all Apache-2.0). The upstream file
    modeling/bagel/modeling_utils.py is CC BY-NC 4.0 (DiT-derived) and is
    NOT ported; its standard components are re-derived from Apache/MIT
    sources in modeling/layers.py (see the family NOTICE). Model weights
    are CC BY-NC 4.0, mirrored byte-identically with attribution at
    huggingface.co/LibreYOLO/SenseNovaVision7b; the loader prints the
    non-commercial notice before every automatic download.

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.

OMDet-Turbo / Transformers (Apache License 2.0)
    Path:    libreyolo/models/openvocab/omdet_turbo.py
    License: licenses/Apache-2.0.txt
    Source:  https://github.com/huggingface/transformers
    Architecture: https://github.com/om-ai-lab/OmDet

    LibreYOLO ships no OMDet-Turbo model source. The adapter calls the
    Apache-2.0 Transformers OmDetTurboForObjectDetection implementation, its
    processor, and its post-processing. Apache-2.0 checkpoint assets are
    mirrored unchanged from omlab/omdet-turbo-swin-tiny-hf revision
    7fe93cecfb770c4d76cf71163956221249cab566 at LibreYOLO/LibreOMDetTurbot.

OV-DEIM (Apache License 2.0)
    Path:    libreyolo/models/openvocab/ov_deim.py,
             libreyolo/models/openvocab/ovdeim/
    License: licenses/Apache-2.0.txt
    Source:  https://github.com/wleilei/OV-DEIM
             (pinned at commit dfbf394672407b7f837ec08e7d68e8127548b254)

    The vendored architecture modules are taken from OV-DEIM under
    Apache-2.0, with the RT-DETR (Apache-2.0, Copyright (c) 2023 lyuwenyu)
    and DEIMv2 (Apache-2.0) lineage headers preserved. Licensing was
    confirmed by the upstream author (wleilei/OV-DEIM#4). See
    docs/provenance/ov_deim.md.

SegFormer / transformers (Apache License 2.0)
    Path:    libreyolo/models/segformer/
    License: libreyolo/models/segformer/NOTICE  (Apache-2.0 code source)
    Source:  https://github.com/huggingface/transformers

    The SegFormer architecture (libreyolo/models/segformer/nn.py) is a
    PyTorch port derived from (and in parts copied from) HuggingFace
    Transformers' Apache-2.0 modeling_segformer.py, not from NVIDIA's
    original non-permissive NVlabs/SegFormer repository (cited for
    attribution only).

    The semantic training recipe it uses -- the resize_crop /
    cat_max_ratio sampling in libreyolo/data/semantic_dataset.py and the
    ADE20K hyper-parameters in SegformerConfig -- is derived from
    open-mmlab/mmsegmentation (Apache-2.0). See THIRD_PARTY_NOTICES.txt.

    WEIGHTS ARE NON-COMMERCIAL. The pretrained ADE20K checkpoints
    (LibreSegformer{b0..b5}-sem) are converted from NVIDIA's SegFormer
    release, whose license permits redistribution but restricts use to
    research or evaluation only. They are not covered by LibreYOLO's
    permissive license. See libreyolo/models/segformer/NOTICE.

Depth Anything 3 (Apache License 2.0)
    Path:    libreyolo/models/depth_anything3/_vendor/
    License: libreyolo/models/depth_anything3/NOTICE
    Source:  https://github.com/ByteDance-Seed/Depth-Anything-3
    Commit:  41736238f5bced4debf3f2a12375d2466874866d

    The DA3MONO-LARGE ViT-L encoder and DPT head are adapted from ByteDance
    Seed's Apache-2.0 implementation. DINOv2 subcomponents retain Meta
    Platforms Apache-2.0 headers. Package-relative imports and dependency-free
    tensor reshapes do not alter learned parameters or monocular outputs.

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. The SIDD denoising weights are trained on the Smartphone
    Image Denoising Dataset (SIDD), which is distributed under the MIT License.

Real-ESRGAN / BasicSR (BSD-3-Clause / Apache-2.0)
    Path:    libreyolo/models/realesrgan/, libreyolo/postprocess/realesrgan.py
    License: libreyolo/models/realesrgan/NOTICE
    Source:  https://github.com/xinntao/Real-ESRGAN (BSD-3-Clause),
             https://github.com/XPixelGroup/BasicSR (Apache-2.0)

    The LibreRealESRGAN super-resolution architectures (RRDBNet,
    SRVGGNetCompact, pixel_unshuffle in libreyolo/models/realesrgan/nn.py) are a
    native PyTorch implementation whose lineage is BasicSR (Apache-2.0,
    Copyright 2018-2022 BasicSR Authors). The seam-free tiled forward in
    libreyolo/models/realesrgan/utils.py is ported from Real-ESRGAN's inference
    helper (BSD-3-Clause, Copyright (c) 2021 Xintao Wang). The released
    Real-ESRGAN generator weights (RealESRGAN_x4plus, RealESRGAN_x2plus,
    realesr-general-x4v3) are distributed under the BSD-3-Clause License;
    LibreYOLO mirrors converted checkpoints with this provenance stated in the
    model cards.

PaddleOCR / PP-OCRv5 (Apache-2.0)
    Path:    libreyolo/models/ppocr/, libreyolo/postprocess/ppocr.py
    License: libreyolo/models/ppocr/NOTICE
    Source:  https://github.com/PaddlePaddle/PaddleOCR

    The LibrePPOCR text detection + recognition family is a native PyTorch
    port of PP-OCRv5 from PaddleOCR (Apache-2.0, Copyright (c) 2020
    PaddlePaddle Authors): the PP-LCNetV3 and PP-HGNetV2-B4 backbones,
    RSEFPN/LKPAN necks, DB heads, SVTR sequence encoder, CTC head, DB quad
    postprocess, and CTC greedy decode. The published LibrePPOCR checkpoints
    are converted from the official Apache-2.0 PP-OCRv5 training checkpoints
    and embed the PP-OCRv5 recognition dictionary as charset metadata.

SwinIR (Apache-2.0)
    Path:    libreyolo/models/swinir/
    License: libreyolo/models/swinir/NOTICE
    Source:  https://github.com/JingyunLiang/SwinIR
             commit 6545850fbf8df298df73d81f3e8cba638787c8bd

    The LibreSwinIR generator is a modified port of the official SwinIR
    network_swinir.py implementation. Upstream parameter names are preserved
    for strict loading of the Apache-2.0 released checkpoints. LibreYOLO adds
    restore-task integration, preprocessing, postprocessing, validation, and
    checkpoint metadata.

Darknet YOLOv1 / YOLOv2 / YOLOv3 / YOLOv4 (public domain)
    Path:    libreyolo/models/darknet/, libreyolo/models/yolo1|yolo2|yolo3|yolo4/
    License: libreyolo/models/darknet/cfgs/NOTICE  (public domain)
    Source:  https://github.com/pjreddie/darknet  (YOLOv1/v2/v3)
             https://github.com/AlexeyAB/darknet   (YOLOv4)

    The LibreYOLO1/2/3/4 families reproduce the YOLOv1, 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.

D-FINE-seg (Apache License 2.0)
    Path:    libreyolo/models/dfine/, libreyolo/postprocess/dfine.py
    Source:  https://github.com/ArgoHA/D-FINE-seg

    LibreDFINE segment support adapts D-FINE-seg's mask decoder/head,
    mask matching, mask loss, and mask postprocess references. The
    D-FINE-seg repository is Apache-2.0; its maintainer approved reuse
    with attribution in ArgoHA/D-FINE-seg#70.

EdgeTAM runtime preprocessing and Transformers checkpoint converter (Apache License 2.0)
    Path:    libreyolo/models/sam/edgetam.py,
             weights/convert_edgetam_weights.py
    License: licenses/Apache-2.0.txt
    Source:  https://github.com/facebookresearch/EdgeTAM
             commit 7711e012a30a2402c4eaab637bdb00a521302c91
    Converter: https://github.com/huggingface/transformers
             commit bd37c453544e83eb875ed3608980a1660376007a
    Payload reference: https://huggingface.co/yonigozlan/EdgeTAM-hf
             revision c266ce53b3fc00f0f495b583f6a116c4e57f53bb

    The EdgeTAM checkpoint key mapping is adapted from Hugging Face
    Transformers' Apache-2.0 converter. LibreYOLO replaces its direct pickle
    load with the repository's safe untrusted-checkpoint loader, pins and
    verifies both source and reference artifact digests, and builds a
    provenance-complete mirror payload. The runtime adapter uses Transformers
    for the model architecture and reproduces the pinned EdgeTAM square image
    transform and prompt-coordinate scaling from sam2/utils/transforms.py.
    The mirror's hash-pinned configuration and processor files are copied from
    the Apache-2.0-declared payload reference; learned tensors are independently
    converted from the official checkpoint and checked exactly.

------------------------------------------------------------------------
Pretrained model weights
------------------------------------------------------------------------

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.
