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Sponsored by
APIMart
zeromodels / eomt_large_ade20k_semantic_512
Model's Last Updated: August 27 2026
image-segmentation
Introduction of eomt_large_ade20k_semantic_512
Model Details of eomt_large_ade20k_semantic_512
See our collection for all versions of EoMT.
Run EoMT with Keras 3: JAX, PyTorch, or TensorFlow
kerasformers/eomt_large_ade20k_semantic_512
Paper: Your ViT is Secretly an Image Segmentation Model (arXiv:2503.19108) · HF Papers
EoMT (Encoder-only Mask Transformer) keeps segmentation inside a plain ViT: learned query tokens are concatenated with patch tokens and run through the same ViT blocks. No pixel decoder, no deformable attention decoder.
For more details on the model, please go to the upstream model card .
Pure-
Keras 3
conversion of
tue-mps/ade20k_semantic_eomt_large_512
for
kerasformers
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
semantic
checkpoint (
EoMTUniversalSegment
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from kerasformers.models.eomt import EoMTUniversalSegment, EoMTImageProcessor
model = EoMTUniversalSegment.from_weights("kerasformers/eomt_large_ade20k_semantic_512")
processor = EoMTImageProcessor.from_weights("kerasformers/eomt_large_ade20k_semantic_512")
image = Image.open("your_image.jpg").convert("RGB")
output = model(processor(image)["pixel_values"], training=False)
result = processor.post_process_semantic_segmentation(
output, target_size=(image.height, image.width)
)
print(result["segmentation"].shape)
Load any EoMT variant the same way with
from_weights("kerasformers/<variant>")
:
| Variant | Hub | Task |
|---|---|---|
eomt_small_coco_panoptic_640
|
kerasformers/eomt_small_coco_panoptic_640
|
panoptic |
eomt_base_coco_panoptic_640
|
kerasformers/eomt_base_coco_panoptic_640
|
panoptic |
eomt_large_coco_panoptic_640
|
kerasformers/eomt_large_coco_panoptic_640
|
panoptic |
eomt_large_coco_instance_640
|
kerasformers/eomt_large_coco_instance_640
|
instance |
eomt_large_ade20k_semantic_512
|
kerasformers/eomt_large_ade20k_semantic_512
|
semantic |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / kerasformers. - Use the post-processor that matches the checkpoint task (panoptic / instance / semantic).
- See EoMT docs and Loading Weights .
-
Community / upstream weights:
EoMTUniversalSegment.from_weights("hf:tue-mps/ade20k_semantic_eomt_large_512").
Special Thanks
A huge thank you to the TU/e MPS EoMT authors for creating and releasing these models.
License: MIT.
Runs of zeromodels eomt_large_ade20k_semantic_512 on huggingface.co
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More Information About eomt_large_ade20k_semantic_512 huggingface.co Model
Url of eomt_large_ade20k_semantic_512
eomt_large_ade20k_semantic_512 huggingface.co Url
Provider of eomt_large_ade20k_semantic_512 huggingface.co
Other API from zeromodels
Updated:August 27 2026
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