README.md

metadata

pipeline_tag: image-segmentation
license: mit
base_model: facebook/mask2former-swin-base-coco-instance
library_name: kerasformers
tags:
  - keras
  - kerasformers
  - mask2former
  - instance-segmentation
  - image-segmentation
  - arxiv:2112.01527
  - pytorch
  - jax
  - tf

See our collection for all versions of Mask2Former.

Run Mask2Former with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/mask2former-swin-base-coco-instance

Paper: Masked-attention Mask Transformer for Universal Image Segmentation (arXiv:2112.01527) · HF Papers

Mask2Former improves MaskFormer with masked attention in the transformer decoder, restricting cross-attention to predicted mask regions for sharper boundaries and stronger universal segmentation.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of facebook/mask2former-swin-base-coco-instance for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is a instance checkpoint (Mask2FormerUniversalSegment) (trained for instance; architecture is universal).

✨ Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
from kerasformers.models.mask2former import Mask2FormerUniversalSegment, Mask2FormerImageProcessor

model = Mask2FormerUniversalSegment.from_weights("kerasformers/mask2former-swin-base-coco-instance")
processor = Mask2FormerImageProcessor.from_weights("kerasformers/mask2former-swin-base-coco-instance")

image = Image.open("your_image.jpg").convert("RGB")
output = model(processor(image)["pixel_values"], training=False)
result = processor.post_process_panoptic_segmentation(
    output, target_size=(image.height, image.width)
)
print(result["segmentation"].shape)

Load any Mask2Former variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub	Task
mask2former-swin-tiny-coco-instance	kerasformers/mask2former-swin-tiny-coco-instance	instance
mask2former-swin-small-coco-instance	kerasformers/mask2former-swin-small-coco-instance	instance
mask2former-swin-base-coco-instance	kerasformers/mask2former-swin-base-coco-instance	instance
mask2former-swin-large-coco-instance	kerasformers/mask2former-swin-large-coco-instance	instance
mask2former-swin-tiny-coco-panoptic	kerasformers/mask2former-swin-tiny-coco-panoptic	panoptic
mask2former-swin-tiny-ade-semantic	kerasformers/mask2former-swin-tiny-ade-semantic	semantic

Tips

• Set KERAS_BACKEND before importing Keras / kerasformers.
• The task suffix is what the checkpoint was trained for; post-process accordingly.
• See Mask2Former docs and Loading Weights.
• Community / upstream weights: Mask2FormerUniversalSegment.from_weights("hf:facebook/mask2former-swin-base-coco-instance").

Special Thanks

A huge thank you to the Facebook AI Research Mask2Former authors for creating and releasing these models.

License: MIT.