|
--- |
|
license: mit |
|
tags: |
|
- vision |
|
- 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, 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). |
|
|
|
![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_dinat_large") |
|
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_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(semantic_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(instance_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(panoptic_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} |
|
} |
|
``` |
|
|