đ Mask2Former
The Mask2Former model is trained on COCO instance segmentation (base-sized version, Swin backbone). It offers a unified solution for various image segmentation tasks.
đ Quick Start
The Mask2Former model, trained on COCO instance segmentation, is a powerful tool for image segmentation. It 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 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 improvements:
- Replacing the pixel decoder with a more advanced multi - scale deformable attention Transformer.
- Adopting a Transformer decoder with masked attention to boost performance without introducing additional computation.
- Improving training efficiency by calculating the loss on subsampled points instead of whole masks.

đ 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 instance segmentation. See the model hub to look for other
fine - tuned versions on a task that interests you.
đģ Usage Examples
Basic Usage
import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-coco-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-coco-instance")
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)
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
result = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
predicted_instance_map = result["segmentation"]
For more code examples, we refer to the documentation.
đ License
License: other
Property |
Details |
Tags |
vision, image - segmentation |
Datasets |
coco |
Widget Examples |
- src: http://images.cocodataset.org/val2017/000000039769.jpg, example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg, example_title: Castle |