Add LibreSegformer ADE20K weights (non-commercial, NVIDIA Source Code License)

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LICENSE

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+NVIDIA Source Code License for SegFormer
+
+1. Definitions
+
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+
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+non-commercially. Notwithstanding the foregoing, NVIDIA and its affiliates may use the Work and any derivative
+works commercially. As used herein, “non-commercially” means for research or evaluation purposes only.
+
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+cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then
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LibreSegformerb0-sem.pt

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+oid sha256:37a5915c12d5b9feb36eed7f7b4711e51bbfe4ea69d2b62a2725d3c11e6d1bbf
+size 15086397

NOTICE

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+LibreSegformer weights
+----------------------
+
+These weights are derived from NVIDIA's SegFormer release
+(https://github.com/NVlabs/SegFormer), specifically the checkpoint
+published at https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512.
+Copyright (c) 2021, NVIDIA Corporation & affiliates. All rights reserved.
+
+Licensed under the NVIDIA Source Code License for SegFormer. A complete copy
+of that license is included in this repository as LICENSE. The license permits
+redistribution of the work and of derivative works, provided the license
+travels with them and attribution notices are retained, but it LIMITS USE TO
+NON-COMMERCIAL PURPOSES ("research or evaluation purposes only", Section 3.3),
+and Section 3.2 carries that limitation forward into every derivative work.
+
+These weights are therefore NON-COMMERCIAL ONLY. They are NOT covered by
+LibreYOLO's permissive license.
+
+Modification: state-dict key remapping only (the upstream `segformer.` encoder
+prefix becomes `encoder.`), plus LibreYOLO checkpoint metadata. Learned
+parameters are NVIDIA's, unchanged; the converted checkpoint reproduces
+upstream logits bit-exactly.
+
+The model was fine-tuned on ADE20K (scene parsing, 150 classes), whose own
+image terms restrict use to non-commercial research and education.
+
+Citation:
+  Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P.
+  "SegFormer: Simple and Efficient Design for Semantic Segmentation with
+  Transformers." NeurIPS 2021.

README.md

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+---
+license: other
+license_name: nvidia-source-code-license-segformer
+license_link: https://github.com/NVlabs/SegFormer/blob/master/LICENSE
+library_name: libreyolo
+pipeline_tag: image-segmentation
+datasets:
+  - scene_parse_150
+tags:
+  - semantic-segmentation
+  - segformer
+  - ade20k
+  - non-commercial
+---
+
+# LibreSegformerb0-sem
+
+SegFormer MiT-b0 (3.8M params) with the all-MLP decode head, fine-tuned on
+ADE20K (150 classes) at 512x512, repackaged for
+[LibreYOLO](https://github.com/LibreYOLO/libreyolo).
+
+> ## ⚠️ NON-COMMERCIAL WEIGHTS
+>
+> These weights are **not** covered by LibreYOLO's permissive license. They
+> derive from NVIDIA's SegFormer release, licensed under the
+> [NVIDIA Source Code License](https://github.com/NVlabs/SegFormer/blob/master/LICENSE),
+> which restricts **use** to **non-commercial research or evaluation purposes
+> only** (Section 3.3). That restriction is carried into every derivative work
+> (Section 3.2) and it binds you, the downloader, not just LibreYOLO.
+>
+> The LibreYOLO **code** and the SegFormer **architecture** are unrestricted, as
+> is any model you train from scratch with them. Only these pretrained weights
+> are limited. For commercial use, train from scratch:
+> `LibreSegformer(size="b0", nb_classes=N).train(data="your.yaml")`.
+
+## Usage
+
+```python
+from libreyolo import LibreSegformer
+
+model = LibreSegformer("LibreSegformerb0-sem.pt")
+results = model.predict("image.jpg")
+mask = results[0].semantic_mask      # dense 150-class ADE20K mask
+```
+
+## Source
+
+Converted from [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512).
+Copyright (c) 2021, NVIDIA Corporation & affiliates.
+Licensed under the NVIDIA Source Code License for SegFormer.
+
+Architecture from "SegFormer: Simple and Efficient Design for Semantic
+Segmentation with Transformers" (Xie et al., NeurIPS 2021). LibreYOLO's
+implementation is an independent port of the Apache-2.0 reference in
+HuggingFace Transformers, with no runtime dependency on it.
+
+## Modifications
+
+State-dict key remapping only (upstream `segformer.` encoder prefix becomes
+`encoder.`), plus LibreYOLO checkpoint metadata. Learned parameters are
+unchanged: the converted checkpoint reproduces upstream logits **bit-exactly**
+(max abs difference 0.0 in float64). See
+[`weights/convert_segformer_weights.py`](https://github.com/LibreYOLO/libreyolo/blob/dev/weights/convert_segformer_weights.py).
+
+## Accuracy
+
+Single-scale ADE20K val mIoU as reported by the SegFormer authors for this
+checkpoint: **37.4**. Numbers are the upstream authors', measured with their
+evaluation protocol; LibreYOLO's `val` uses a fixed-canvas letterbox and will
+not reproduce them exactly.
+
+## License
+
+NVIDIA Source Code License for SegFormer (**non-commercial**). See
+[`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE).