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| license: agpl-3.0 |
| datasets: |
| - rafaelpadilla/coco2017 |
| - nateraw/kitti |
| - Chris1/cityscapes |
| - dgural/bdd100k |
| metrics: |
| - precision |
| - f1 |
| - recall |
| pipeline_tag: object-detection |
| --- |
| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| Butter is a novel 2D object detection framework designed to enhance hierarchical feature representations for improved detection robustness. |
| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** [Xiaojian Lin et al.] |
| - **Funded by:** [National Natural Science Foundation of China] |
| - **Model type:** [Object Detection] |
| - **License:** [AGPL-3.0 license] |
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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** [https://github.com/Aveiro-Lin/Butter] |
| - **Paper:** [https://www.arxiv.org/pdf/2507.13373] |
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| ## Uses |
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| The training and inference details, as well as the environment configuration, can be found in our GitHub repository, where a comprehensive description is provided. The model’s performance metrics and training details are thoroughly described in the paper we provide. |
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