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README.md

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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.