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pipeline_tag: object-detection
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# DiffusionDet
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pipeline_tag: object-detection
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# 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)
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- 🚀 [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).
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📄 Check out the paper: [LoRA for Cross-Domain Few-Shot Object Detection](https://huggingface.co/papers/2504.06330)
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