| ---
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| license: other
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| license_name: cityscapes-non-commercial
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| license_link: https://www.cityscapes-dataset.com/license/
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| library_name: libreyolo
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| pipeline_tag: image-segmentation
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| tags:
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| - image-segmentation
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| - semantic-segmentation
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| - ppliteseg
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| - stdc
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| - cityscapes
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| datasets:
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| - cityscapes
|
| ---
|
|
|
| # LibrePPLiteSegt75-sem
|
|
|
| > **NON-COMMERCIAL WEIGHTS.** These weights are trained on Cityscapes. The
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| > [Cityscapes license](https://www.cityscapes-dataset.com/license/) permits
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| > distributing abstract derivative models from which the dataset cannot be
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| > recovered, and restricts the dataset **and its derivatives** to
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| > non-commercial use. The restriction applies to this checkpoint, not to
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| > LibreYOLO's MIT code or to the PP-LiteSeg architecture. Weights you train
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| > from scratch on your own data carry none of it; a fine-tune started from
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| > this checkpoint inherits it. Read the restriction before you download.
|
|
|
| PP-LiteSeg t75 (STDC1 backbone, native 768x1536 canvas), a real-time
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| semantic segmentation model for Cityscapes' 19 classes, repackaged for
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| [LibreYOLO](https://github.com/LibreYOLO/libreyolo).
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|
|
| ```python
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| from libreyolo import LibreYOLO
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|
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| model = LibreYOLO("LibrePPLiteSegt75-sem.pt")
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| result = model.predict("street.jpg")[0]
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| mask = result.semantic_mask.data # (H, W) class IDs on the original canvas
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| ```
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|
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| This is a genuinely rectangular model: it runs at 768x1536 (height x width),
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| not a square canvas. Published Cityscapes validation mIoU for the source
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| checkpoint is 77.56. LibreYOLO has not re-measured that number; it is quoted
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| from the source release, and exact raw-logit parity does not by itself
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| reproduce an end-to-end dataset evaluation.
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|
|
| ## Source
|
|
|
| Derived from [Deci-AI/super-gradients](https://github.com/Deci-AI/super-gradients)
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| at commit `63de22c404d5740f34f7706c302b37fce3c8fe5d`.
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| Copyright (c) 2021-2024 Deci AI. Licensed under the Apache License 2.0.
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|
|
| Source artifact: [`pp_lite_t_seg75_cityscapes.pth`](https://d2gjn4b69gu75n.cloudfront.net/models/pp_lite_t_seg75_cityscapes.pth),
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| SHA-256 `1fdd809572a1b3168727ed0dea32da287c9917ed0ebbfdf8ecab87a3116733f6` (verified before conversion).
|
|
|
| The STDC backbone lineage comes from
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| [MichaelFan01/STDC-Seg](https://github.com/MichaelFan01/STDC-Seg) at commit
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| `59ff37fbd693b99972c76fcefe97caa14aeb619f`, MIT.
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| Copyright (c) 2021 Mingyuan Fan. The architecture was cross-checked against
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| [PaddlePaddle/PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) at commit
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| `3c4db66de1d9d59d0628ed87590b6308a2f4aa2a`, Apache-2.0; no PaddleSeg code was
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| copied.
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|
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| Paper: [PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model](https://arxiv.org/abs/2204.02681).
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|
|
| ## Modifications
|
|
|
| State-dict key remapping only: the upstream `net` payload with exactly one
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| `module.` DDP prefix stripped, wrapped in LibreYOLO v1.0 checkpoint metadata.
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| Learned parameters are unchanged, and the three training auxiliary heads are
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| retained so the checkpoint stays trainable. The port reproduces the pinned
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| upstream exactly (`max_abs_diff == 0.0` on the main logits). See
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| `weights/convert_ppliteseg_weights.py` and `weights/parity_ppliteseg.py` in the
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| [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).
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|
|
| ## License
|
|
|
| The **code** is Apache-2.0 (super-gradients) with MIT STDC lineage; both
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| license texts are in [`LICENSE`](./LICENSE), and attribution is in
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| [`NOTICE`](./NOTICE).
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|
|
| The **weights in this repository are non-commercial** under the Cityscapes
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| dataset terms linked above. Downstream users are responsible for complying with
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| them.
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|
|
