MaskFormer

MaskFormer模型是在COCO千兆分割数据集上训练的（基础尺寸版本，使用Swin骨干网络）。该模型是在 Per-Pixel Classification is Not All You Need for Semantic Segmentation 论文中提出的，并于 this repository 首次发布。

免责声明：发布MaskFormer模型的团队并未为该模型撰写模型卡片，因此本模型卡片由Hugging Face团队撰写。

模型描述

MaskFormer使用相同范例来处理实例分割、语义分割和全景分割：通过预测一组掩码和相应的标签来完成这三个任务。因此，这三个任务都被视为实例分割任务。

使用目的和限制

您可以使用这个特定的检查点来进行语义分割。如果您想寻找其他与您感兴趣的任务进行微调的版本，请参阅 model hub 。

使用方法

以下是如何使用该模型的方法：

from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests

# load MaskFormer fine-tuned on COCO panoptic segmentation
feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-coco")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits

# you can pass them to feature_extractor for postprocessing
result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_panoptic_map = result["segmentation"]

有关更多代码示例，请参阅 documentation 。