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--- |
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license: other |
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license_name: aplux-model-farm-license |
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license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf |
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pipeline_tag: image-segmentation |
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tags: |
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- AIoT |
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- QNN |
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--- |
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## Mask2Former: Semantic Segmentation |
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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. |
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### Source model |
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- Input shape: 1x3x384x384 |
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- Number of parameters: 42.01M |
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- Model size: 201M |
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- Output shape: [1x100x134],[1x100x96x96] |
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The source model can be found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/mask2former) |
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## Performance Reference |
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Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) |
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## Inference & Model Conversion |
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Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) |
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## License |
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- Source Model: [APACHE-2.0](https://github.com/huggingface/transformers/blob/main/LICENSE) |
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- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf) |
