Maskformer Swin Tiny Ade

🚀 MaskFormer

MaskFormer是一個在ADE20k語義分割數據集上訓練的模型（微小版本，採用Swin骨幹網絡）。它在論文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。該模型使用相同的範式處理實例、語義和全景分割任務，通過預測一組掩碼和相應的標籤，將這3種任務都視為實例分割任務。

🚀 快速開始

本模型適用於語義分割任務。你可以在模型中心查找其他針對你感興趣任務的微調版本。

以下是使用該模型的示例代碼：

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-tiny-ade")
inputs = feature_extractor(images=image, return_tensors="pt")

model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-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]

更多代碼示例請參考文檔。

✨ 主要特性

MaskFormer使用相同的範式處理實例、語義和全景分割任務，即通過預測一組掩碼和相應的標籤，將所有這3種任務都視為實例分割任務。

📄 許可證

許可證類型：other

📦 相關信息

⚠️ 重要提示

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。