| LibreSegformer weights
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| These weights are derived from NVIDIA's SegFormer release
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| (https://github.com/NVlabs/SegFormer), specifically the checkpoint
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| published at https://huggingface.co/nvidia/segformer-b3-finetuned-ade-512-512.
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| Copyright (c) 2021, NVIDIA Corporation & affiliates. All rights reserved.
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| Licensed under the NVIDIA Source Code License for SegFormer. A complete copy
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| of that license is included in this repository as LICENSE. The license permits
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| redistribution of the work and of derivative works, provided the license
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| travels with them and attribution notices are retained, but it LIMITS USE TO
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| NON-COMMERCIAL PURPOSES ("research or evaluation purposes only", Section 3.3),
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| and Section 3.2 carries that limitation forward into every derivative work.
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| These weights are therefore NON-COMMERCIAL ONLY. They are NOT covered by
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| LibreYOLO's permissive license.
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|
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| Modification: state-dict key remapping only (the upstream `segformer.` encoder
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| prefix becomes `encoder.`), plus LibreYOLO checkpoint metadata. Learned
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| parameters are NVIDIA's, unchanged; the converted checkpoint reproduces
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| upstream logits bit-exactly.
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| The model was fine-tuned on ADE20K (scene parsing, 150 classes), whose own
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| image terms restrict use to non-commercial research and education.
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| Citation:
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| Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., and Luo, P.
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| "SegFormer: Simple and Efficient Design for Semantic Segmentation with
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| Transformers." NeurIPS 2021.
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| |
