Mask2former Swin Tiny Ade Semantic

🚀 Mask2Former

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

🚀 Quick Start

The Mask2Former model is designed to handle instance, semantic, and panoptic segmentation tasks. It was introduced in the paper Masked - attention Mask Transformer for Universal Image Segmentation and first released in this repository.

✨ Features

• Unified Paradigm: Addresses instance, semantic, and panoptic segmentation using the same approach by predicting a set of masks and corresponding labels.
• Performance and Efficiency: Outperforms the previous SOTA MaskFormer through advanced techniques such as replacing the pixel decoder, adopting a masked - attention Transformer decoder, and improving training efficiency.

📦 Installation

No specific installation steps are provided in the original document.

💻 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-tiny-ade-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-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

Model description

Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi - scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

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.

🔧 Technical Details

The Mask2Former model uses a unified approach for different segmentation tasks. It replaces the pixel decoder with a multi - scale deformable attention Transformer, adopts a masked - attention Transformer decoder, and calculates the loss on subsampled points to improve performance and efficiency.

📄 License

The license for this model is "other".