license: apache-2.0
library_name: transformers
pipeline_tag: image-segmentation
tags:
- image-segmentation
- instance-segmentation
- vision
- acaua
datasets:
- coco
base_model: facebook/mask2former-swin-tiny-coco-instance
Mask2Former Swin-Tiny (COCO Instance) — acaua mirror
Apache-2.0 mirror hosted under CondadosAI/ for use with the acaua computer vision library.
This is a safetensors-only mirror of the upstream Meta AI Research weights at the pinned commit shown below. The model.safetensors file is byte-identical to upstream; we do not modify weights or configuration. The legacy pytorch_model.bin (pickle format) that upstream ships alongside safetensors has been deliberately removed from this mirror for security hygiene — pickle loads can execute arbitrary code, and transformers auto-prefers safetensors when both are present, so removing it has zero functional impact on downstream users.
The purpose of the mirror is license hygiene: acaua's core promise is that every shipped weight has an auditable, declared Apache-2.0 upstream. Mirroring lets us pin a specific revision so the audit claim stays verifiable even if upstream rewrites history.
Provenance
| Upstream repo | facebook/mask2former-swin-tiny-coco-instance |
| Upstream commit SHA | 22c4a2f15dc88149b8b8d9f4d42c54431fbd66f6 |
| Upstream commit date | 2023-09-11 |
| Declared license | Apache-2.0 (upstream YAML frontmatter) |
| Paper | Cheng et al., "Masked-attention Mask Transformer for Universal Image Segmentation", CVPR 2022, arXiv:2112.01527 |
| Official code | facebookresearch/Mask2Former (MIT) |
| Backbone | Swin-Tiny, pretrained on ImageNet-1k (per upstream model card) |
| Mirrored on | 2026-04-17 |
| Mirrored by | CondadosAI/acaua |
Usage via acaua
import acaua
model = acaua.Model.from_pretrained("CondadosAI/mask2former_swin_tiny_coco_instance")
results = model.predict("image.jpg")
for r in results:
print(r.boxes, r.labels, r.scores, r.masks.shape)
Usage via 🤗 Transformers
This mirror is drop-in compatible with the upstream Facebook repo:
from transformers import AutoModelForUniversalSegmentation, AutoImageProcessor
model = AutoModelForUniversalSegmentation.from_pretrained(
"CondadosAI/mask2former_swin_tiny_coco_instance"
)
processor = AutoImageProcessor.from_pretrained(
"CondadosAI/mask2former_swin_tiny_coco_instance"
)
License and attribution
Redistributed under Apache License 2.0, consistent with the upstream HF model card declaration. The reference implementation at facebookresearch/Mask2Former is MIT-licensed; the weights as distributed by facebook/* on Hugging Face are declared Apache-2.0.
See NOTICE for required attribution to upstream contributors (Meta AI Research / FAIR, Mask2Former authors, Swin Transformer authors).
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={CVPR},
year={2022}
}
@inproceedings{liu2021swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
booktitle={ICCV},
year={2021}
}