Maskformer Swin Base Ade

🚀 MaskFormer

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

🚀 Quick Start

The MaskFormer model is designed for semantic segmentation tasks. It 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

• Addresses instance, semantic, and panoptic segmentation using the same paradigm by predicting a set of masks and corresponding labels.
• Treats all three tasks as instance segmentation.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

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

Advanced Usage

For more code examples, we refer to the documentation.

📚 Documentation

Model description

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.

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.

📄 License

The license for this model is "other".