Mask2former Swin Tiny Cityscapes Semantic

🚀 Mask2Former

The Mask2Former model is trained on Cityscapes semantic segmentation (tiny-sized version, Swin backbone). It offers a unified solution for image segmentation tasks.

🚀 Quick Start

The Mask2Former model, trained on Cityscapes semantic segmentation, is a powerful tool for image segmentation. It can handle instance, semantic, and panoptic segmentation tasks.

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


# load Mask2Former fine-tuned on Cityscapes semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-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)

✨ 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. It achieves this 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 introducing additional computation, and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

📚 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 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.

How to use

Here is how to use this model:

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


# load Mask2Former fine-tuned on Cityscapes semantic segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-semantic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-cityscapes-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)

For more code examples, we refer to the documentation.

📄 License

The license for this model is other.