LibreSegformerb0 Sem
nvidia-source-code-license-segformer
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 14,4 MB
- Versão
- vLibre Segformerb0 sem
- Atualizado
- 10 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
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.
⚠️ 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, 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
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. 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.
Accuracy
ADE20K val (2000 images), single scale:
| mIoU | |
|---|---|
LibreYOLO model.val(data="ade20k.yaml") | 36.5 |
| Upstream authors, reported | 37.4 |
LibreYOLO's validator letterboxes to a fixed square canvas; the authors evaluate with a ratio-preserving resize, which is worth roughly a point. The converted weights reproduce the upstream model's logits bit-exactly (max abs difference 0.0 in float64), so any remaining difference is evaluation protocol, not conversion.
License
NVIDIA Source Code License for SegFormer (non-commercial). See
LICENSE and NOTICE.
Fontes
1 fonte
Fontes
1 fonteVerificado 10 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- 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.c...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library libreyolo · Repo LibreYOLO/LibreSegformerb0-sem
