Oneformer Coco Dinat Large

🚀 OneFormer

OneFormer是一个在COCO数据集（大尺寸版本，Dinat主干网络）上训练的模型。它由Jain等人在论文OneFormer: One Transformer to Rule Universal Image Segmentation中提出，并首次在此仓库中发布。该模型可用于图像分割任务，通过单一架构和模型实现多任务的高效处理。

🚀 快速开始

你可以使用这个特定的检查点进行语义、实例和全景分割。若要查找在其他数据集上微调的版本，请查看模型中心。

✨ 主要特性

• OneFormer是首个多任务通用图像分割框架。
• 仅需使用单一通用架构、单一模型在单一数据集上进行一次训练，就能在语义、实例和全景分割任务中超越现有的专门模型。
• 使用任务令牌使模型专注于特定任务，使架构在训练时具有任务导向性，在推理时具有任务动态性，且仅需一个模型。

💻 使用示例

基础用法

from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
from PIL import Image
import requests
url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/coco.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_coco_dinat_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_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(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"]

更多示例请参考文档。

📚 详细文档

引用

@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}
    }

📄 许可证

本项目采用MIT许可证。

💡 使用建议

若要使用不同的数据集进行分割任务，可在模型中心查找其他微调版本的OneFormer模型。