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kerasformers/
oneformer_cityscapes_swin_large

Name: oneformer_cityscapes_swin_large
Availability: InStock

kerasformers

keras

oneformer

universal-segmentation

shi-labs/oneformer_cityscapes_swin_large

finetune

mit

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Run OneFormer with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

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

python

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_BACKEND before 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.

kerasformers/oneformer_cityscapes_swin_large