readme: ---
  license: mit
  tags:
  - vision
  - image-segmentation
  - universal-image-segmentation
  datasets:
  - scene_parse_150
  widget:
  - src: https://praeclarumjj3.github.io/files/ade20k.jpeg
    example_title: House
  - src: https://praeclarumjj3.github.io/files/demo_2.jpg
    example_title: Airplane
  - src: https://praeclarumjj3.github.io/files/coco.jpeg
    example_title: Person
  ---
  
  # OneFormer
  
  OneFormer model trained on the ADE20k dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer).
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_teaser.png)
  
  ## Model description
  
  OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model.
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_architecture.png)
  
  ## Intended uses & limitations
  
  You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset.
  
  ### How to use
  
  Here is how to use this model:
  
  ```python
  from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
  from PIL import Image
  import requests
  url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg"
  image = Image.open(requests.get(url, stream=True).raw)
  
  # Loading a single model for all three tasks
  processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
  model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
  
  # Semantic Segmentation
  semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
  semantic_outputs = model(**semantic_inputs)
  # pass through image_processor for postprocessing
  predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
  
  # Instance Segmentation
  instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt")
  instance_outputs = model(**instance_inputs)
  # pass through image_processor for postprocessing
  predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
  
  # Panoptic Segmentation
  panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt")
  panoptic_outputs = model(**panoptic_inputs)
  # pass through image_processor for postprocessing
  predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
  ```
  
  For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer).
  
  ### Citation
  
  ```bibtex
  @article{jain2022oneformer,
        title={{OneFormer: One Transformer to Rule Universal Image Segmentation}},
        author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi},
        journal={arXiv}, 
        year={2022}
      }
  ```
  