README.md

---
license: other
license_name: aplux-model-farm-license
license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
pipeline_tag: image-segmentation
tags:
- AIoT
- QNN
---

![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250613112054_seg.png&w=640&q=75)

## Mask2Former: Semantic Segmentation

Mask2Former, proposed by Meta AI in 2022, is a unified framework for image segmentation (instance, semantic, and panoptic). It leverages a Transformer decoder with learnable "mask queries" to dynamically generate segmentation masks, eliminating dependency on anchors or proposals. The model integrates multi-scale feature enhancement, combining high-resolution details with deep semantics, and optimizes query-feature interaction via cross-attention. Achieving state-of-the-art results on COCO and ADE20K, Mask2Former excels in complex scenes and small-object segmentation. Its end-to-end architecture supports flexible deployment in autonomous driving, medical imaging, and remote sensing, advancing unified high-performance segmentation solutions.

### Source model

- Input shape: 1x3x384x384
- Number of parameters: 42.01M
- Model size: 201M
- Output shape: [1x100x134],[1x100x96x96]

The source model can be found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/mask2former)

## Performance Reference

Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)

## Inference & Model Conversion

Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)

## License

- Source Model: [APACHE-2.0](https://github.com/huggingface/transformers/blob/main/LICENSE)

- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf)