Instructions to use HichTala/DiffusionDet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HichTala/DiffusionDet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="HichTala/DiffusionDet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HichTala/DiffusionDet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
datasets:
- detection-datasets/coco
pipeline_tag: object-detection
Model Card for DiffusionDet
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.
π§ Uses
You can load and use the model with Hugging Face's π€ transformers or via the original repository.
This model has been adapted for cross-domain few-shot object detection using LoRA (Low-Rank Adaptation). π Check out the paper: LoRA for Cross-Domain Few-Shot Object Detection