README.md

metadata

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 (code). 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 (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.

License

DEIMv2 code and weights are released by Intellindust AI Lab under a non-commercial research license — see LICENSE.md. Commercial use requires a separate license from Intellindust.

Citation

@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}
}