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zeromodels / oneformer_cityscapes_swin_large
Model's Last Updated: August 27 2026
image-segmentation
Introduction of oneformer_cityscapes_swin_large
Model Details of oneformer_cityscapes_swin_large
See our collection for all versions of OneFormer.
Run OneFormer with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/oneformer_cityscapes_swin_large
Paper: OneFormer: One Transformer to Rule Universal Image Segmentation (arXiv:2211.06220) · HF Papers
OneFormer trains one model on semantic, instance, and panoptic segmentation jointly, conditioned on a text token that names the task. At inference you pass
task="semantic"
,
"instance"
, or
"panoptic"
to the same weights. The processor combines an image processor and a tokenizer for that task token.
For more details on the model, please go to SHI Labs' original model card .
Pure-
Keras 3
conversion of
shi-labs/oneformer_cityscapes_swin_large
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
universal segmentation
checkpoint (
OneFormerUniversalSegment
) trained on
Cityscapes
(19 classes, Swin-Large). All three tasks share these weights.
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.oneformer import (
OneFormerUniversalSegment,
OneFormerProcessor,
)
model = OneFormerUniversalSegment.from_weights("zeromodels/oneformer_cityscapes_swin_large")
processor = OneFormerProcessor.from_weights("zeromodels/oneformer_cityscapes_swin_large")
image = Image.open("your_image.jpg").convert("RGB")
# task is an argument: "semantic" | "instance" | "panoptic"
inputs = processor(images=image, task="panoptic")
output = model(inputs)
result = processor.post_process_panoptic_segmentation(
output, target_size=(image.height, image.width)
)
print(result["segmentation"].shape)
Load any OneFormer variant the same way with
from_weights("zeromodels/<variant>")
:
| Variant | Hub | Dataset | Backbone |
|---|---|---|---|
oneformer_ade20k_swin_tiny
|
zeromodels/oneformer_ade20k_swin_tiny
|
ADE20K | Swin-Tiny |
oneformer_ade20k_swin_large
|
zeromodels/oneformer_ade20k_swin_large
|
ADE20K | Swin-Large |
oneformer_coco_swin_large
|
zeromodels/oneformer_coco_swin_large
|
COCO | Swin-Large |
oneformer_cityscapes_swin_large
|
zeromodels/oneformer_cityscapes_swin_large
|
Cityscapes | Swin-Large |
Tips
-
Set
KERAS_BACKENDbefore importing Keras / zeromodels. -
Prefer
OneFormerProcessor.from_weights(...)so image size and tokenizer match the variant. -
Pick the task at call time via
processor(..., task=...); the checkpoint vocabulary comes from the training set, not the task. - See OneFormer docs and Loading Weights .
-
Community / upstream safetensors still work via the
hf:prefix, e.g.OneFormerUniversalSegment.from_weights("hf:shi-labs/oneformer_cityscapes_swin_large").
Special Thanks
A huge thank you to the SHI Labs OneFormer authors for creating and releasing these models.
License: MIT.
Runs of zeromodels oneformer_cityscapes_swin_large on huggingface.co
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More Information About oneformer_cityscapes_swin_large huggingface.co Model
Url of oneformer_cityscapes_swin_large
oneformer_cityscapes_swin_large huggingface.co Url
Provider of oneformer_cityscapes_swin_large huggingface.co
Other API from zeromodels
Updated:August 27 2026
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