LibreRetinaNet -- third-party attribution
==========================================

Source: https://github.com/pytorch/vision
Commit: 336d36e8db990a905498c73933e35231876e28bc (torchvision v0.26.0)
License: BSD-3-Clause
Copyright (c) Soumith Chintala 2016

The native inference graph in nn.py is derived from torchvision's RetinaNet,
classification and regression heads, anchor generator, box coder, transform,
and FPN-backbone builders. LibreYOLO retains the upstream state-dict layout and
adds its own factory, checkpoint metadata, validation, class mapping, and
export/backend integration. Training-only matching and focal-loss code is not
included.

The two official COCO checkpoints used for parity are not distributed in the
LibreYOLO source tree. Their separate LibreYOLO Hugging Face mirrors use
BSD-3-Clause on an explicitly disclosed implied basis: the checkpoints are
released by the BSD-licensed project but do not carry per-object license files.
Torchvision's README warns that pretrained-model terms may derive from training
data and that users remain responsible for determining permission for their
use case. Each mirror ships this BSD text and repeats that caveat; it must not
be described as an explicit checkpoint-specific grant.

BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,

All rights reserved.

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