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--- |
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license: mit |
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tags: |
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- vision |
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- image-segmentation |
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datasets: |
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- ydshieh/coco_dataset_script |
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widget: |
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- src: https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg |
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example_title: Person |
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- src: https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/demo_2.jpg |
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example_title: Airplane |
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- src: https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/demo.jpeg |
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example_title: Corgi |
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--- |
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# OneFormer |
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OneFormer model trained on the COCO dataset (large-sized version, Dinat 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). |
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## Model description |
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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. |
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## Intended uses & limitations |
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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. |
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### How to use |
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Here is how to use this model: |
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```python |
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from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation |
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from PIL import Image |
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import requests |
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url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.jpeg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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# Loading a single model for all three tasks |
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processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_coco_dinat_large") |
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model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_dinat_large") |
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# Semantic Segmentation |
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semantic_inputs = processor(images=image, ["semantic"] return_tensors="pt") |
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semantic_outputs = model(**semantic_inputs) |
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# pass through image_processor for postprocessing |
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predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
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# Instance Segmentation |
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instance_inputs = processor(images=image, ["instance"] return_tensors="pt") |
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instance_outputs = model(**instance_inputs) |
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# pass through image_processor for postprocessing |
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predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] |
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# Panoptic Segmentation |
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panoptic_inputs = processor(images=image, ["panoptic"] return_tensors="pt") |
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panoptic_outputs = model(**panoptic_inputs) |
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# pass through image_processor for postprocessing |
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predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] |
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``` |
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For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer). |
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### Citation |
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```bibtex |
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@article{jain2022oneformer, |
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title={{OneFormer: One Transformer to Rule Universal Image Segmentation}}, |
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author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi}, |
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journal={arXiv}, |
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year={2022} |
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} |
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``` |
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