🚀 MaskFormer
MaskFormer是一個在ADE20k語義分割數據集上訓練的模型(大型版本,採用Swin骨幹網絡)。該模型解決了實例、語義和全景分割問題,為圖像分割領域提供了高效的解決方案。
🚀 快速開始
你可以使用此特定檢查點進行語義分割。若想尋找針對其他感興趣任務的微調版本,可查看模型中心。
from transformers import MaskFormerImageProcessor, 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)
processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-ade")
inputs = processor(images=image, return_tensors="pt")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
outputs = model(**inputs)
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
更多代碼示例請參考文檔。
✨ 主要特性
MaskFormer通過預測一組掩碼和相應的標籤,以相同的範式處理實例、語義和全景分割問題。因此,所有這3個任務都被視為實例分割任務。

💻 使用示例
基礎用法
from transformers import MaskFormerImageProcessor, 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)
processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-ade")
inputs = processor(images=image, return_tensors="pt")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
outputs = model(**inputs)
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
高級用法
如需更多高級使用場景,請參考文檔中的代碼示例。
📚 詳細文檔
MaskFormer模型在論文 Per-Pixel Classification is Not All You Need for Semantic Segmentation 中被提出,並首次在 此倉庫 中發佈。
需注意,發佈MaskFormer的團隊並未為此模型編寫模型卡片,此模型卡片由Hugging Face團隊編寫。
📄 許可證
許可證類型:other