MaskFormer

在ADE20k语义分割数据集上训练的MaskFormer模型（基本尺寸版本，使用Swin骨干）。该模型首次发布于 this repository 。

免责声明：发布MaskFormer的团队并未为此模型编写模型卡片，因此此模型卡片由Hugging Face团队撰写。

模型描述

MaskFormer通过预测一组掩码和相应的标签来处理实例、语义和全景分割，因此将这三个任务都视为实例分割。

预期用途与限制

您可以使用此特定检查点进行语义分割。请查看 model hub ，以查找您感兴趣的其他微调版本的任务。

如何使用

使用此模型的方法如下:

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

url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade")
inputs = feature_extractor(images=image, return_tensors="pt")

model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
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
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]

更多代码示例请参阅 documentation 。