Maskformer Swin Large Coco

🚀 MaskFormer

A MaskFormer model trained on COCO panoptic segmentation (large - sized version, Swin backbone). It offers a unified approach to instance, semantic, and panoptic segmentation.

🚀 Quick Start

The MaskFormer model was introduced in the paper Per - Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.

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

✨ Features

MaskFormer 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.

📚 Documentation

Intended uses & limitations

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

How to use

Here is how to use this model:

from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests

# load MaskFormer fine-tuned on COCO panoptic segmentation
processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-coco")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-coco")

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

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
result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_panoptic_map = result["segmentation"]

For more code examples, we refer to the documentation.

📄 License

License: other