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license: apache-2.0
library_name: pytorch
tags:
- object-detection
- computer-vision
- discrete-diffusion
- wacv-2027
datasets:
- detection-datasets/coco
---
# DDM-Det: Fast Object Detection via One-Step Discrete Diffusion
Official pretrained model checkpoint for **Fast Object Detection via One-Step Discrete Diffusion**, accepted at **WACV 2027**.
DDM-Det formulates object detection as discrete token generation and performs detection in a single reverse diffusion step.
## Results
Evaluated on COCO 2017 validation using a ResNet-50 backbone.
| AP | AP50 | AP75 | FPS |
|---|---|---|---|
| 40.50 | 60.58 | 42.29 | 58.82 |
*FPS is the paper-reported end-to-end inference speed, measured under the experimental setup described in the paper.*
## Model Checkpoint
- **File:** `model_final.pth`
- **Backbone:** ResNet-50
- **Dataset:** COCO 2017
- **Framework:** PyTorch and Detectron2
## Code and Usage
The complete implementation, installation instructions, and evaluation commands are available on GitHub:
**[DDM-Det GitHub Repository](https://github.com/neeresh/one-step-discrete-diffusion)**
## Citation
If you use this model in your research, please cite:
*Fast Object Detection via One-Step Discrete Diffusion.* IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2027.
The official publication and BibTeX citation will be added when available.
## License
Apache License 2.0. |