OneFormer OneFormer model trained on the Cityscapes dataset (large sized version, Swin backbone). It was introduced in the paper OneFormer: One Transformer to Rule Universal Image Segmentation by Jain et al. and first released in this repository. 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. Intended uses & limitations You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the model hub to look for other fine tuned versions on a different dataset. How to use Here is how to use this model: For more examples, please refer to the documentation. Citation
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy