Add documentation

README.md

@@ -9,3 +9,66 @@ widget:
 - src: https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/cityscapes.png
   example_title: Cityscapes
 ---
+
+# OneFormer
+
+OneFormer model trained on the Cityscapes 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/oneformer_teaser.png)
+
+## Model description
+
+OneFormer is the first multi-task universal image segmentation framework based on transformers. OneFormer needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing frameworks 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/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 OneFormerFeatureExtractor, OneFormerForUniversalSegmentation
+from PIL import Image
+import requests
+url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/cityscapes.png"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Loading a single model for all three tasks
+feature_extractor = OneFormerFeatureExtractor.from_pretrained("shi-labs/oneformer_cityscapes_dinat_large")
+model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_cityscapes_dinat_large")
+
+# Semantic Segmentation
+semantic_inputs = feature_extractor(images=image, ["semantic"] return_tensors="pt")
+semantic_outputs = model(**semantic_inputs)
+# pass through feature_extractor for postprocessing
+predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+
+# Instance Segmentation
+instance_inputs = feature_extractor(images=image, ["instance"] return_tensors="pt")
+instance_outputs = model(**instance_inputs)
+# pass through feature_extractor for postprocessing
+predicted_instance_map = feature_extractor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"]
+
+# Panoptic Segmentation
+panoptic_inputs = feature_extractor(images=image, ["panoptic"] return_tensors="pt")
+panoptic_outputs = model(**panoptic_inputs)
+# pass through feature_extractor for postprocessing
+predicted_semantic_map = feature_extractor.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}
+    }
+```