---
title: DEIMv2-S Object Detection
emoji: 🎯
colorFrom: gray
colorTo: purple
sdk: gradio
sdk_version: 6.31.0
python_version: "3.12"
app_file: app.py
startup_duration_timeout: 30m
short_description: DEIMv2-S real-time COCO object detection (DINOv3)
license: other
license_name: deimv2-research-only
license_link: https://github.com/Intellindust-AI-Lab/DEIMv2/blob/main/LICENSE.md
---

# DEIMv2-S · Real-Time Object Detection Meets DINOv3

Interactive demo for **DEIMv2-S** — the flagship checkpoint of
[Real-Time Object Detection Meets DINOv3](https://arxiv.org/abs/2509.20787)
([code](https://github.com/Intellindust-AI-Lab/DEIMv2)). DEIMv2 couples a
DINOv3-pretrained ViT backbone with a **Spatial Tuning Adapter (STA)** and a
simplified DETR-style decoder under **Dense One-to-One matching**; the S model
reaches **50.9 COCO AP at 9.7M parameters**, running end-to-end (NMS-free).

Weights: [Intellindust/DEIMv2_DINOv3_S_COCO](https://huggingface.co/Intellindust/DEIMv2_DINOv3_S_COCO)
(loaded from this Space, no gated access required — the `vit_tiny` backbone is
trained from scratch and fully contained in the checkpoint).

## How it works

- Preprocessing follows the authors' `tools/inference/torch_inf.py`:
  `Resize(640×640)` → `ToTensor` → **ImageNet normalization** (used by all
  DINOv3/ViT-backbone variants).
- The full `engine/` package from the official repo is bundled for the model
  definition; `app.py` replicates the `DEIMv2` wrapper class from the authors'
  `hf_models.ipynb` and loads weights via `PyTorchModelHubMixin`.
- Boxes are scaled back to the **original image size** and scores are sigmoid
  confidences; each COCO category is drawn in its own color (per-category HSV
  palette from the authors' notebook).
- Runs on ZeroGPU (`spaces.GPU`); inference is a single forward pass of a 9.7M
  parameter detector, so latency is well under a second on an A100.

## Example images

- `examples/authors_example.jpg` — the demo image shipped in the official
  DEIMv2 repository (non-commercial research license).
- All other examples are CC0 images from
  [linoyts/repo-to-space-example-inputs](https://huggingface.co/datasets/linoyts/repo-to-space-example-inputs).

## License

DEIMv2 code and weights are released by Intellindust AI Lab under a
**non-commercial research license** — see
[LICENSE.md](https://github.com/Intellindust-AI-Lab/DEIMv2/blob/main/LICENSE.md).
Commercial use requires a separate license from Intellindust.

## Citation

```bibtex
@article{huang2025deimv2,
  title   = {Real-Time Object Detection Meets DINOv3},
  author  = {Huang, Shihua and Hou, Yongjie and Liu, Longfei and Yu, Xuanlong and Shen, Xi},
  journal = {arXiv preprint arXiv:2509.20787},
  year    = {2025}
}
```