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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
<!-- Provide a quick summary of what the model is/does. -->
Butter is a novel 2D object detection framework designed to enhance hierarchical feature representations for improved detection robustness.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Xiaojian Lin et al.]
- **Funded by:** [National Natural Science Foundation of China]
- **Model type:** [Object Detection]
- **License:** [AGPL-3.0 license]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/Aveiro-Lin/Butter]
- **Paper:** [https://www.arxiv.org/pdf/2507.13373]
## Uses
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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