EoMT

Install

EoMT needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally. The task suffix in the filename (-sem, -seg, -panoptic) selects the task, and LibreYOLO() infers it from that filename so no task= argument is needed.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreEoMTl-sem.pt")result = model(SAMPLE_IMAGE, save=True) mask = result.semantic_maskprint(mask.data.shape)   # (H, W) class idsprint(mask.classes)      # sorted class ids present in the image

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The -seg suffix in the filename selects the instance task, so no# task argument is needed here.model = LibreYOLO("LibreEoMTl-seg.pt")result = model(SAMPLE_IMAGE, save=True) print(result.boxes.xyxy)print(result.masks.data.shape)

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreEoMTl-panoptic.pt")result = model(SAMPLE_IMAGE, save=True) pan = result.panopticprint(pan.data.shape)       # (H, W) segment idsprint(pan.segments_info)    # [{"id": ..., "category_id": ...}, ...]

libreyolo predict model=LibreEoMTl-sem.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

Semantic segmentation fills result.semantic_mask, a (H, W) array of class ids on .data. Instance segmentation fills result.boxes and result.masks, the same shape every other segmentation family returns. Panoptic segmentation fills result.panoptic: a (H, W) segment-id map on .data, plus .segments_info, a list of {"id", "category_id"} dicts, one per segment. conf filters query selection; iou has no effect on the semantic task, since it argmaxes per pixel with no NMS step. See prediction for sources, streaming and result handling.

Variants

Three encoder sizes, s/b/l, all DINOv2-backed. The semantic checkpoint is trained on ADE20K at 512 px; the instance and panoptic checkpoints are trained on COCO at 640 px, with a second instance checkpoint trained at 1280 px. Upstream ships DINOv2 instance-segmentation weights only at size l; s and b are published for semantic and panoptic only. DINOv3-backed EoMT variants exist upstream but are not shipped here, because they depend on gated non-commercial DINOv3 weights.

LibreYOLO does not train EoMT: train() raises NotImplementedError for this family, which the support tier above marks as inference only.

Validate

val() dispatches by task. Semantic returns metrics/mIoU and metrics/pixel_accuracy. Instance segmentation returns the same mask and box mAP keys as other segmentation families. Panoptic returns Panoptic Quality as metrics/PQ, split into metrics/SQ (segmentation quality) and metrics/RQ (recognition quality), plus metrics/PQ_things and metrics/PQ_stuff.

from libreyolo import LibreYOLO model = LibreYOLO("LibreEoMTl-sem.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mIoU"])print(metrics["metrics/pixel_accuracy"])

from libreyolo import LibreYOLO model = LibreYOLO("LibreEoMTl-seg.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95(M)"])   # masksprint(metrics["metrics/mAP50-95(B)"])   # boxes

from libreyolo import LibreYOLO model = LibreYOLO("LibreEoMTl-panoptic.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/PQ"])print(metrics["metrics/SQ"], metrics["metrics/RQ"])

libreyolo val model=LibreEoMTl-sem.pt data=my-dataset.yaml

Export

Only the semantic task exports today: instance and panoptic segmentation call export() and get NotImplementedError, because their query-mask output has no runtime export contract yet. An exported semantic artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results.

from libreyolo import LibreYOLO model = LibreYOLO("LibreEoMTl-sem.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreEoMTl-sem.pt format=onnxlibreyolo export model=LibreEoMTl-sem.pt format=tensorrt half=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreEoMTl-sem.onnx")result = model(SAMPLE_IMAGE) print(result.semantic_mask.data.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@inproceedings{kerssies2025eomt,
  author    = {Kerssies, Tommie and Cavagnero, Niccol\`{o} and Hermans, Alexander and Norouzi, Narges and Averta, Giuseppe and Leibe, Bastian and Dubbelman, Gijs and {de Geus}, Daan},
  title     = {{Your ViT is Secretly an Image Segmentation Model}},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
}