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Run Growth:
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Sponsored by
Agentkit
zeromodels / eomt_base_coco_panoptic_640
Model's Last Updated: August 27 2026
image-segmentation
Introduction of eomt_base_coco_panoptic_640
Model Details of eomt_base_coco_panoptic_640
See our collection for all versions of EoMT.
Run EoMT with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/eomt_base_coco_panoptic_640
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/coco_panoptic_eomt_base_640_2x
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
panoptic
checkpoint (
EoMTUniversalSegment
).
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.eomt import EoMTUniversalSegment, EoMTImageProcessor
model = EoMTUniversalSegment.from_weights("zeromodels/eomt_base_coco_panoptic_640")
processor = EoMTImageProcessor.from_weights("zeromodels/eomt_base_coco_panoptic_640")
image = Image.open("your_image.jpg").convert("RGB")
output = model(processor(image)["pixel_values"], training=False)
result = processor.post_process_panoptic_segmentation(
output, target_size=(image.height, image.width)
)
print(result["segmentation"].shape)
Load any EoMT variant the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub | Task |
|---|---|---|
eomt_small_coco_panoptic_640
|
zeromodels/eomt_small_coco_panoptic_640
|
panoptic |
eomt_base_coco_panoptic_640
|
zeromodels/eomt_base_coco_panoptic_640
|
panoptic |
eomt_large_coco_panoptic_640
|
zeromodels/eomt_large_coco_panoptic_640
|
panoptic |
eomt_large_coco_instance_640
|
zeromodels/eomt_large_coco_instance_640
|
instance |
eomt_large_ade20k_semantic_512
|
zeromodels/eomt_large_ade20k_semantic_512
|
semantic |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. - 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/coco_panoptic_eomt_base_640_2x").
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_base_coco_panoptic_640 on huggingface.co
131
Total runs
2
24-hour runs
3
3-day runs
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7-day runs
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30-day runs
More Information About eomt_base_coco_panoptic_640 huggingface.co Model
Url of eomt_base_coco_panoptic_640
eomt_base_coco_panoptic_640 huggingface.co Url
Provider of eomt_base_coco_panoptic_640 huggingface.co
Other API from zeromodels
Updated:August 27 2026
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