Update model card with paper, code and usage info

README.md

@@ -1,10 +1,63 @@
 ---
+license: apache-2.0
+pipeline_tag: object-detection
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+- object-detection
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# DEIMv2-Pico
+
+DEIMv2 is an evolution of the DEIM (Dense One-to-One DETR) framework, leveraging features from DINOv3 for real-time object detection. The **DEIMv2-Pico** variant is an ultra-lightweight model designed for mobile and edge deployment, achieving 38.5 AP on COCO with only 1.5 million parameters.
+
+- **Paper:** [Real-Time Object Detection Meets DINOv3](https://huggingface.co/papers/2509.20787)
+- **GitHub Repository:** [https://github.com/Intellindust-AI-Lab/DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2)
+- **Project Page:** [https://intellindust-ai-lab.github.io/projects/DEIMv2/](https://intellindust-ai-lab.github.io/projects/DEIMv2/)
+
+## Model Description
+DEIMv2 establishes new state-of-the-art results for real-time DETRs across various model sizes. For the ultra-lightweight variants like Pico, the model utilizes an HGNetv2 backbone with depth and width pruning, a Lite encoder, and a simplified decoder. This design enables a superior performance-cost trade-off, matching the performance of larger models like YOLOv10-Nano with significantly fewer parameters.
+
+## Usage
+
+You can load this model using the `PyTorchModelHubMixin` integration. To use it, you need to have the official [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) source code in your Python path to import the necessary modules.
+
+```python
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+# The following imports require the source code from the DEIMv2 repository
+from engine.backbone import HGNetv2
+from engine.deim import LiteEncoder, DEIMTransformer
+from engine.deim.postprocessor import PostProcessor
+
+class DEIMv2(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, config):
+        super().__init__()
+        self.backbone = HGNetv2(**config["HGNetv2"])
+        self.encoder = LiteEncoder(**config["LiteEncoder"])
+        self.decoder = DEIMTransformer(**config["DEIMTransformer"])
+        self.postprocessor = PostProcessor(**config["PostProcessor"])
+
+    def forward(self, x, orig_target_sizes):
+        x = self.backbone(x)
+        x = self.encoder(x)
+        x = self.decoder(x)
+        x = self.postprocessor(x, orig_target_sizes)
+        return x
+
+# Load the model from the Hub
+model = DEIMv2.from_pretrained("Intellindust/DEIMv2_HGNetv2_PICO_COCO")
+model.eval()
+```
+
+## Citation
+If you find DEIMv2 useful in your research, please cite:
+```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},
+  year={2025}
+}
+```