EoMT-L semantic segmentation weights for ADE20K, converted to the LibreYOLO
checkpoint format.
Derived from
tue-mps/ade20k_semantic_eomt_large_512,
the DINOv2-based ADE20K semantic checkpoint for EoMT.
EoMT was introduced in "Your ViT is Secretly an Image Segmentation Model" by
Tommie Kerssies, Niccolo Cavagnero, Alexander Hermans, Narges Norouzi,
Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, and Daan de Geus. The official
implementation is at
tue-mps/eomt and is
licensed under the MIT License.
The DINOv2 backbone is Apache-2.0. LibreYOLO ships only the DINOv2-based
ADE20K EoMT-L semantic checkpoint here. DINOv3 EoMT variants are intentionally
excluded because they depend on gated non-commercial DINOv3 weights.
State-dict normalization and LibreYOLO v1.0 metadata wrapping only. Learned
parameters are unchanged. See
weights/convert_eomt_weights.py in the
LibreYOLO source repository.
1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibreEoMTl-sem.pt")
4result = model("image.jpg")
5semantic_mask = result.semantic_mask
The parity target for this checkpoint is ADE20K val mIoU 58.4 +/- 0.5. ADE20K
is not redistributed in this repository. Users are responsible for complying
with ADE20K dataset terms when validating or fine-tuning.
MIT License for EoMT weights. See
LICENSE. DINOv2 and the
Hugging Face Transformers EoMT runtime are Apache-2.0; see
NOTICE
for attribution.
