| --- |
| 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. |
