README.md

---
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](https://github.com/CondadosAI/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`](https://huggingface.co/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](https://arxiv.org/abs/2112.01527) |
| Official code | [`facebookresearch/Mask2Former`](https://github.com/facebookresearch/Mask2Former) (MIT) |
| Backbone | Swin-Tiny, pretrained on ImageNet-1k (per upstream model card) |
| Mirrored on | 2026-04-17 |
| Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) |

## Usage via acaua

```python
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:

```python
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`](./NOTICE) for required attribution to upstream contributors (Meta AI Research / FAIR, Mask2Former authors, Swin Transformer authors).

## Citation

```bibtex
@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}
}
```