---
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

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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]


### 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.