Mask2former Swin Base Ade Semantic

🚀 Mask2Former

The Mask2Former model is trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It offers a unified approach for instance, semantic, and panoptic segmentation.

🚀 Quick Start

The Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone) was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository.

Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team.

✨ Features

• Unified Segmentation Paradigm: Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm by predicting a set of masks and corresponding labels.
• Performance and Efficiency: It outperforms the previous SOTA, MaskFormer, both in terms of performance and efficiency through several key improvements.
  • Advanced Transformer: Replaces the pixel decoder with a more advanced multi - scale deformable attention Transformer.
  • Masked Attention: Adopts a Transformer decoder with masked attention to boost performance without introducing additional computation.
  • Efficient Training: Improves training efficiency by calculating the loss on subsampled points instead of whole masks.

💻 Usage Examples

Basic Usage

import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation

# load Mask2Former fine-tuned on ADE20k semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-ade-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-ade-semantic")

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

with torch.no_grad():
    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 processor for postprocessing
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)

Advanced Usage

For more code examples, we refer to the documentation.

📚 Documentation

Intended uses & limitations

You can use this particular checkpoint for panoptic segmentation. See the model hub to look for other fine - tuned versions on a task that interests you.

📄 License

License: other