LibreDeepLabv3mv3-sem

DeepLabv3 semantic segmentation with dilated MobileNetV3-Large, output stride 16, repackaged for LibreYOLO. It predicts background plus 20 Pascal VOC-named foreground classes from a checkpoint trained on the matching COCO subset. This is DeepLabv3, not DeepLabv3+; there is no decoder or CRF.

from libreyolo import LibreYOLO

model = LibreYOLO("LibreDeepLabv3mv3-sem.pt")
result = model.predict("image.jpg")
mask = result.semantic_mask.data

Source

Derived from pytorch/vision at commit 336d36e8db990a905498c73933e35231876e28bc (torchvision v0.26.0). Copyright (c) Soumith Chintala 2016 and torchvision contributors. The source implementation is BSD-3-Clause.

Official checkpoint: deeplabv3_mobilenet_v3_large-fc3c493d.pth
Bytes: 44356159
SHA-256: fc3c493d68e89cc31ef488c803d5d7dd2f3190fb570598faa49fef69be8e5e70
Published mIoU / pixel accuracy: 60.3 / 91.2.

The published metrics use torchvision's aspect-preserving evaluation preset. LibreYOLO uses a fixed 520x520 stretch deployment contract, followed by ImageNet normalization and restoration of the output mask to the source canvas, so end-to-end metrics can differ.

Modifications and verification

Conversion removes only the training-time aux_classifier.* tensors and adds LibreYOLO v1.0 checkpoint metadata. Every retained runtime tensor and state-dict key is unchanged. The native 520x520 logits are bit-exact against the pinned torchvision implementation before postprocessing (max_abs_diff == 0.0).

The fixed-shape deployment graph was also tested through LibreYOLO's unified backend:

• ONNX Runtime CPU: 100% identical public mask pixels; maximum logit difference 3.06e-5.
• TorchScript: bit-exact logits and 100% identical public mask pixels.
• OpenVINO CPU: 99.9876% identical public mask pixels using the runtime's default reduced-precision execution hint.
• TensorRT 10.16 FP32 on RTX 5070 Ti: 99.9851% identical public mask pixels.

The converted file has 44325189 bytes and SHA-256 fb83a67bca845817d816d139af6fb6a4b9d809c0a813ebcfcb1e2a5fbd222682. See docs/provenance/deeplabv3.md and weights/convert_deeplabv3_weights.py in the LibreYOLO source repository.

License

The checkpoint publisher did not attach a separate per-object license file. This mirror applies the releasing project's BSD-3-Clause license on an implied, not publisher-confirmed, basis. Torchvision warns that pretrained models may have licenses or terms derived from training data and that users must determine whether they have permission for their use case. COCO annotations are CC BY 4.0; source images retain their individual Flickr terms. See LICENSE and NOTICE.