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| library_name: transformers |
| license: mit |
| datasets: |
| - detection-datasets/coco |
| pipeline_tag: object-detection |
| --- |
| |
| # Model Card for DiffusionDet |
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| DiffusionDet is a diffusion-based object detection model that formulates object detection as a denoising diffusion process. It iteratively refines noisy box predictions to generate high-quality detection outputs. This approach provides a flexible and unified framework for object detection, offering advantages over traditional proposal-based methods. |
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| ## 🔧 Uses |
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| You can load and use the model with Hugging Face's 🤗 `transformers` or via the original repository. |
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| - 📦 [Original GitHub repo](github.com/pierlj/fsdiffusiondet) |
| - 🚀 [Few-shot cross-domain adaptation repo](https://github.com/ShoufaChen/DiffusionDet) |
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| This model has been adapted for cross-domain few-shot object detection using LoRA (Low-Rank Adaptation). |
| 📄 Check out the paper: [LoRA for Cross-Domain Few-Shot Object Detection](https://huggingface.co/papers/2504.06330) |
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