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Mask2former Swin Base Ade

> Cheng et al., 2022 — Masked-attention Mask Transformer for Universal Image Segmentation (arXiv:2112.01527)

TipoOtherVersiónRevision d6b293dCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
408 MB
Versión
vRevision d6b293d
Actualizado
14 oct 2026
Descargas
0
Fuente
Hugging Face

Descripción de la fuente

Mask2Former (Swin-Base)

Cheng et al., 2022 — Masked-attention Mask Transformer for Universal Image Segmentation (arXiv:2112.01527)

Lucid port of facebook/mask2former-swin-base-ade-semantic, converted to Lucid-native safetensors.

Available weights

TagmIoUParamsGFLOPsSizeSource
ADE20K (default)53.9106.9M—407.98 MBfacebook

Usage

import lucid.models as models
from lucid.models.weights import Mask2FormerSwinBaseWeights

# default tag
model = models.mask2former_swin_base(pretrained=True)

# explicit tag (enum or string)
model = models.mask2former_swin_base(weights=Mask2FormerSwinBaseWeights.ADE20K)
model = models.mask2former_swin_base(pretrained="ADE20K")

# preprocessing travels with the weights
weights = Mask2FormerSwinBaseWeights.ADE20K
preprocess = weights.transforms()
out = model(preprocess(image)[None])
# SemanticSegmentationOutput: per-pixel class logits (B, C, H, W)
seg = out.logits.argmax(axis=1)  # (B, H, W) class indices

Conversion

Converted from facebook/mask2former-swin-base-ade-semantic via python -m tools.convert_weights mask2former_swin_base --tag ADE20K. Key mapping + numerical parity verified against the source.

License

other — inherited from the original weights.

Citation

@inproceedings{cheng2022mask2former,
  title={Masked-attention Mask Transformer for Universal Image Segmentation},
  author={Cheng, Bowen and Misra, Ishan and Schwing, Alexander G. and Kirillov, Alexander and Girdhar, Rohit},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Fuentes

1 fuente
Hugging FacePrincipalVersión d6b293da03cd8db77edbd203dc4cffa81864a64e
huggingface.co/lucid-dl/mask2former-swin-base-ade/tree/d6b293da03cd8db77edbd203dc4cffa81864a64e

Verificado 26 ago

Artefactos del modelo

1 artefacto

ADE20K/model.safetensors

safetensors · 408 MB · SHA-256 3185a471a980…e447 · Hugging Face

Download file

Extractos de fuentes

2 extractos

--- library_name: lucid license: other tags: - semantic-segmentation - mask2former - lucid datasets: - ade20k pipeline_tag: image-segmentation model-index: - name: mask2former-swin-base results: - task: { type: semantic-segmentation } dataset: { name: ADE20K, type: ade20k } metrics: - { type: mIoU, value: 53.9 } --- # Mask2Former (Swin-Base) > Cheng et al., 2022 — *Masked-attention Mask Transformer for Universal Image Segmentation* (arXiv:2112.01527) [Lucid](https://github.com/ChanLumerico/lucid) port of `facebook/mask2former-swin-base-ade-semantic`, converted to Lucid-native safetensors. ## Available weights | Tag | mIoU | Params | GFLOPs | Size | Source | |---|---|---|---|---|---| | `ADE20K` *(default)* | 53.9 | 106.9M | — | 407.98 MB | facebook | ## Usage ```python import lucid.models as models from lucid.models.weights import Mask2FormerSwinBaseWeights # default tag model = models.mask2former_swin_base(pretrained=True) # explicit tag (enum or string) model = models.mask2former_swin_base(weights=Mask2FormerSwinBaseWeights.ADE20K) model = models.mask2former_swin_base(pretrained="ADE20K") # preprocessing travels with the weights weights = Mask2FormerSwinBaseWeights.ADE20K preprocess = weights.transforms() out = model(preprocess(image)[None]) # SemanticSegmentationOutput: per-pixel class logits (B, C, H, W) seg = out.logits.argmax(axis=1) # (B, H, W) class indices ``` ## Conversion Converted from `facebook/mask2former-swin-base-ade-semantic` via `python -m tools.convert_weights mask2former_swin_base --tag ADE20K`. Key mapping + numerical parity verified against the source. ## License `other` — inherited from the original weights. ## Citation ``` @inproceedings{cheng2022mask2former, title={Masked-attention Mask Transformer for Universal Image Segmentation}, author={Cheng, Bowen and Misra, Ishan and Schwing, Alexander G. and Kirillov, Alexander and Girdhar, Rohit}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022} } ```

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library lucid · Repo lucid-dl/mask2former-swin-base-ade